polygraf-ai/ACL-abstracts · Datasets at Hugging Face

inproceedings

pinter-etal-2017-mimicking

Mimicking Word Embeddings using Subword {RNN}s

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1010/

Pinter, Yuval and Guthrie, Robert and Eisenstein, Jacob

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

102--112

Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist. In this paper, we present MIMICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings. Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level. Intrinsic and extrinsic evaluations demonstrate the power of this simple approach. On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes. It is competitive with (and complementary to) a supervised character-based model in low resource settings.

null

10.18653/v1/D17-1010

null

57,498

inproceedings

asgari-schutze-2017-past

Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1011/

Asgari, Ehsaneddin and Sch{\"utze, Hinrich

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

113--124

We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguistic phenomenon of tense. We produce analysis results for more than 1000 languages, conducting {--} to the best of our knowledge {--} the largest crosslingual computational study performed to date. We extend existing methodology for leveraging parallel corpora for typological analysis by overcoming a limiting assumption of earlier work: We only require that a linguistic feature is overtly marked in a few of thousands of languages as opposed to requiring that it be marked in all languages under investigation.

null

10.18653/v1/D17-1011

null

57,499

inproceedings

hashimoto-tsuruoka-2017-neural

Neural Machine Translation with Source-Side Latent Graph Parsing

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1012/

Hashimoto, Kazuma and Tsuruoka, Yoshimasa

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

125--135

This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.

null

10.18653/v1/D17-1012

null

57,500

inproceedings

weng-etal-2017-neural

Neural Machine Translation with Word Predictions

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1013/

Weng, Rongxiang and Huang, Shujian and Zheng, Zaixiang and Dai, Xinyu and Chen, Jiajun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

136--145

In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence. These vectors are generated by parameters which are updated by back-propagation of translation errors through time. We argue that propagating errors through the end-to-end recurrent structures are not a direct way of control the hidden vectors. In this paper, we propose to use word predictions as a mechanism for direct supervision. More specifically, we require these vectors to be able to predict the vocabulary in target sentence. Our simple mechanism ensures better representations in the encoder and decoder without using any extra data or annotation. It is also helpful in reducing the target side vocabulary and improving the decoding efficiency. Experiments on Chinese-English machine translation task show an average BLEU improvement by 4.53, respectively.

null

10.18653/v1/D17-1013

null

57,501

inproceedings

hoang-etal-2017-towards

Towards Decoding as Continuous Optimisation in Neural Machine Translation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1014/

Hoang, Cong Duy Vu and Haffari, Gholamreza and Cohn, Trevor

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

146--156

We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We reformulate decoding, a discrete optimization problem, into a continuous problem, such that optimization can make use of efficient gradient-based techniques. Our powerful decoding framework allows for more accurate decoding for standard neural machine translation models, as well as enabling decoding in intractable models such as intersection of several different NMT models. Our empirical results show that our decoding framework is effective, and can leads to substantial improvements in translations, especially in situations where greedy search and beam search are not feasible. Finally, we show how the technique is highly competitive with, and complementary to, reranking.

null

10.18653/v1/D17-1014

null

57,502

inproceedings

kitaev-klein-2017-misty

Where is Misty? Interpreting Spatial Descriptors by Modeling Regions in Space

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1015/

Kitaev, Nikita and Klein, Dan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

157--166

We present a model for locating regions in space based on natural language descriptions. Starting with a 3D scene and a sentence, our model is able to associate words in the sentence with regions in the scene, interpret relations such as {\textquoteleft}on top of' or {\textquoteleft}next to,' and finally locate the region described in the sentence. All components form a single neural network that is trained end-to-end without prior knowledge of object segmentation. To evaluate our model, we construct and release a new dataset consisting of Minecraft scenes with crowdsourced natural language descriptions. We achieve a 32{\%} relative error reduction compared to a strong neural baseline.

null

10.18653/v1/D17-1015

null

57,503

inproceedings

rahimi-etal-2017-continuous

Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1016/

Rahimi, Afshin and Baldwin, Timothy and Cohn, Trevor

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

167--176

We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.

null

10.18653/v1/D17-1016

null

57,504

inproceedings

yin-ordonez-2017-obj2text

{O}bj2{T}ext: Generating Visually Descriptive Language from Object Layouts

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1017/

Yin, Xuwang and Ordonez, Vicente

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

177--187

Generating captions for images is a task that has recently received considerable attention. Another type of visual inputs are abstract scenes or object layouts where the only information provided is a set of objects and their locations. This type of imagery is commonly found in many applications in computer graphics, virtual reality, and storyboarding. We explore in this paper OBJ2TEXT, a sequence-to-sequence model that encodes a set of objects and their locations as an input sequence using an LSTM network, and decodes this representation using an LSTM language model. We show in our paper that this model despite using a sequence encoder can effectively represent complex spatial object-object relationships and produce descriptions that are globally coherent and semantically relevant. We test our approach for the task of describing object layouts in the MS-COCO dataset by producing sentences given only object annotations. We additionally show that our model combined with a state-of-the-art object detector can improve the accuracy of an image captioning model.

null

10.18653/v1/D17-1017

null

57,505

inproceedings

lee-etal-2017-end

End-to-end Neural Coreference Resolution

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1018/

Lee, Kenton and He, Luheng and Lewis, Mike and Zettlemoyer, Luke

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

188--197

We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.

null

10.18653/v1/D17-1018

null

57,506

inproceedings

li-jurafsky-2017-neural

Neural Net Models of Open-domain Discourse Coherence

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1019/

Li, Jiwei and Jurafsky, Dan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

198--209

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences. We study both discriminative models that learn to distinguish coherent from incoherent discourse, and generative models that produce coherent text, including a novel neural latent-variable Markovian generative model that captures the latent discourse dependencies between sentences in a text. Our work achieves state-of-the-art performance on multiple coherence evaluations, and marks an initial step in generating coherent texts given discourse contexts.

null

10.18653/v1/D17-1019

null

57,507

inproceedings

wang-etal-2017-affinity

Affinity-Preserving Random Walk for Multi-Document Summarization

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1020/

Wang, Kexiang and Liu, Tianyu and Sui, Zhifang and Chang, Baobao

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

210--220

Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process. The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods.

null

10.18653/v1/D17-1020

null

57,508

inproceedings

marasovic-etal-2017-mention

A Mention-Ranking Model for Abstract Anaphora Resolution

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1021/

Marasovi{\'c}, Ana and Born, Leo and Opitz, Juri and Frank, Anette

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

221--232

Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence{--}antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and {--} if disregarding syntax {--} discriminates candidates using deeper features.

null

10.18653/v1/D17-1021

null

57,509

inproceedings

nguyen-etal-2017-hierarchical

Hierarchical Embeddings for Hypernymy Detection and Directionality

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1022/

Nguyen, Kim Anh and K{\"oper, Maximilian and Schulte im Walde, Sabine and Vu, Ngoc Thang

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

233--243

We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym{--}hyponym distributional hierarchy. Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages. Results on benchmark datasets show that HyperVec outperforms both state-of-the-art unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.

null

10.18653/v1/D17-1022

null

57,510

inproceedings

zhao-etal-2017-ngram2vec

{N}gram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1023/

Zhao, Zhe and Liu, Tao and Li, Shen and Li, Bofang and Du, Xiaoyong

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

244--253

The existing word representation methods mostly limit their information source to word co-occurrence statistics. In this paper, we introduce ngrams into four representation methods: SGNS, GloVe, PPMI matrix, and its SVD factorization. Comprehensive experiments are conducted on word analogy and similarity tasks. The results show that improved word representations are learned from ngram co-occurrence statistics. We also demonstrate that the trained ngram representations are useful in many aspects such as finding antonyms and collocations. Besides, a novel approach of building co-occurrence matrix is proposed to alleviate the hardware burdens brought by ngrams.

null

10.18653/v1/D17-1023

null

57,511

inproceedings

tissier-etal-2017-dict2vec

{D}ict2vec : Learning Word Embeddings using Lexical Dictionaries

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1024/

Tissier, Julien and Gravier, Christophe and Habrard, Amaury

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

254--263

Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, although such resources cover a fraction of words in the vocabulary. In this paper, we propose a new approach, Dict2vec, based on one of the largest yet refined datasource for describing words {--} natural language dictionaries. Dict2vec builds new word pairs from dictionary entries so that semantically-related words are moved closer, and negative sampling filters out pairs whose words are unrelated in dictionaries. We evaluate the word representations obtained using Dict2vec on eleven datasets for the word similarity task and on four datasets for a text classification task.

null

10.18653/v1/D17-1024

null

57,512

inproceedings

su-lee-2017-learning

Learning {C}hinese Word Representations From Glyphs Of Characters

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1025/

Su, Tzu-Ray and Lee, Hung-Yi

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

264--273

In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word representations by character glyphs. The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings. Another contribution in this paper is that we created several evaluation datasets in traditional Chinese and made them public.

null

10.18653/v1/D17-1025

null

57,513

inproceedings

wieting-etal-2017-learning

Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1026/

Wieting, John and Mallinson, Jonathan and Gimpel, Kevin

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

274--285

We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate sentential paraphrases via back-translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training data for learning paraphrastic sentence embeddings. We find that the data quality is stronger than prior work based on bitext and on par with manually-written English paraphrase pairs, with the advantage that our approach can scale up to generate large training sets for many languages and domains. We experiment with several language pairs and data sources, and develop a variety of data filtering techniques. In the process, we explore how neural machine translation output differs from human-written sentences, finding clear differences in length, the amount of repetition, and the use of rare words.

null

10.18653/v1/D17-1026

null

57,514

inproceedings

yu-etal-2017-joint

Joint Embeddings of {C}hinese Words, Characters, and Fine-grained Subcharacter Components

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1027/

Yu, Jinxing and Jian, Xun and Xin, Hao and Song, Yangqiu

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

286--291

Word embeddings have attracted much attention recently. Different from alphabetic writing systems, Chinese characters are often composed of subcharacter components which are also semantically informative. In this work, we propose an approach to jointly embed Chinese words as well as their characters and fine-grained subcharacter components. We use three likelihoods to evaluate whether the context words, characters, and components can predict the current target word, and collected 13,253 subcharacter components to demonstrate the existing approaches of decomposing Chinese characters are not enough. Evaluation on both word similarity and word analogy tasks demonstrates the superior performance of our model.

null

10.18653/v1/D17-1027

null

57,515

inproceedings

gupta-etal-2017-exploiting

Exploiting Morphological Regularities in Distributional Word Representations

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1028/

Gupta, Arihant and Akhtar, Syed Sarfaraz and Vajpayee, Avijit and Srivastava, Arjit and Jhanwar, Madan Gopal and Shrivastava, Manish

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

292--297

We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces. We propose a novel method for generating embeddings of words from their morphological variants using morphological transformation operators. We evaluate this approach on MSR word analogy test set with an accuracy of 85{\%} which is 12{\%} higher than the previous best known system.

null

10.18653/v1/D17-1028

null

57,516

inproceedings

wang-etal-2017-exploiting

Exploiting Word Internal Structures for Generic {C}hinese Sentence Representation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1029/

Wang, Shaonan and Zhang, Jiajun and Zong, Chengqing

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

298--303

We introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures. Our architecture uses two key strategies. The first is a mask gate on characters, learning the relation among characters in a word. The second is a maxpooling operation on words, adaptively finding the optimal mixture of the atomic and compositional word representations. Finally, the proposed architecture is applied to various sentence composition models, which achieves substantial performance gains over baseline models on sentence similarity task.

null

10.18653/v1/D17-1029

null

57,517

inproceedings

herbelot-baroni-2017-high

High-risk learning: acquiring new word vectors from tiny data

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1030/

Herbelot, Aur{\'e}lie and Baroni, Marco

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

304--309

Distributional semantics models are known to struggle with small data. It is generally accepted that in order to learn {\textquoteleft}a good vector' for a word, a model must have sufficient examples of its usage. This contradicts the fact that humans can guess the meaning of a word from a few occurrences only. In this paper, we show that a neural language model such as Word2Vec only necessitates minor modifications to its standard architecture to learn new terms from tiny data, using background knowledge from a previously learnt semantic space. We test our model on word definitions and on a nonce task involving 2-6 sentences' worth of context, showing a large increase in performance over state-of-the-art models on the definitional task.

null

10.18653/v1/D17-1030

null

57,518

inproceedings

sanu-etal-2017-word

Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1031/

Sanu, Joseph and Xu, Mingbin and Jiang, Hui and Liu, Quan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

310--315

In this paper, we propose to learn word embeddings based on the recent fixed-size ordinally forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence into a fixed-size representation. We use FOFE to fully encode the left and right context of each word in a corpus to construct a novel word-context matrix, which is further weighted and factorized using truncated SVD to generate low-dimension word embedding vectors. We evaluate this alternate method in encoding word-context statistics and show the new FOFE method has a notable effect on the resulting word embeddings. Experimental results on several popular word similarity tasks have demonstrated that the proposed method outperforms other SVD models that use canonical count based techniques to generate word context matrices.

null

10.18653/v1/D17-1031

null

57,519

inproceedings

fernandez-etal-2017-vecshare

{V}ec{S}hare: A Framework for Sharing Word Representation Vectors

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1032/

Fernandez, Jared and Yu, Zhaocheng and Downey, Doug

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

316--320

Many Natural Language Processing (NLP) models rely on distributed vector representations of words. Because the process of training word vectors can require large amounts of data and computation, NLP researchers and practitioners often utilize pre-trained embeddings downloaded from the Web. However, finding the best embeddings for a given task is difficult, and can be computationally prohibitive. We present a framework, called VecShare, that makes it easy to share and retrieve word embeddings on the Web. The framework leverages a public data-sharing infrastructure to host embedding sets, and provides automated mechanisms for retrieving the embeddings most similar to a given corpus. We perform an experimental evaluation of VecShare`s similarity strategies, and show that they are effective at efficiently retrieving embeddings that boost accuracy in a document classification task. Finally, we provide an open-source Python library for using the VecShare framework.

null

10.18653/v1/D17-1032

null

57,520

inproceedings

hasan-curry-2017-word

Word Re-Embedding via Manifold Dimensionality Retention

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1033/

Hasan, Souleiman and Curry, Edward

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

321--326

Word embeddings seek to recover a Euclidean metric space by mapping words into vectors, starting from words co-occurrences in a corpus. Word embeddings may underestimate the similarity between nearby words, and overestimate it between distant words in the Euclidean metric space. In this paper, we re-embed pre-trained word embeddings with a stage of manifold learning which retains dimensionality. We show that this approach is theoretically founded in the metric recovery paradigm, and empirically show that it can improve on state-of-the-art embeddings in word similarity tasks 0.5 - 5.0{\%} points depending on the original space.

null

10.18653/v1/D17-1033

null

57,521

inproceedings

lee-chen-2017-muse

{MUSE}: Modularizing Unsupervised Sense Embeddings

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1034/

Lee, Guang-He and Chen, Yun-Nung

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

327--337

This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representation learning or inefficient sense selection. The proposed modular approach, MUSE, implements flexible modules to optimize distinct mechanisms, achieving the first purely sense-level representation learning system with linear-time sense selection. We leverage reinforcement learning to enable joint training on the proposed modules, and introduce various exploration techniques on sense selection for better robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on synonym selection as well as on contextual word similarities in terms of MaxSimC.

null

10.18653/v1/D17-1034

null

57,522

inproceedings

reimers-gurevych-2017-reporting

Reporting Score Distributions Makes a Difference: Performance Study of {LSTM}-networks for Sequence Tagging

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1035/

Reimers, Nils and Gurevych, Iryna

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

338--348

In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant ($p < 10^{-4}$) differences for state-of-the-art systems. For two recent systems for NER, we observe an absolute difference of one percentage point F₁-score depending on the selected seed value, making these systems perceived either as state-of-the-art or mediocre. Instead of publishing and reporting single performance scores, we propose to compare score distributions based on multiple executions. Based on the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we present network architectures that produce both superior performance as well as are more stable with respect to the remaining hyperparameters.

null

10.18653/v1/D17-1035

null

57,523

inproceedings

martins-kreutzer-2017-learning

Learning What`s Easy: Fully Differentiable Neural Easy-First Taggers

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1036/

Martins, Andr{\'e} F. T. and Kreutzer, Julia

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

349--362

We introduce a novel neural easy-first decoder that learns to solve sequence tagging tasks in a flexible order. In contrast to previous easy-first decoders, our models are end-to-end differentiable. The decoder iteratively updates a {\textquotedblleft}sketch{\textquotedblright} of the predictions over the sequence. At its core is an attention mechanism that controls which parts of the input are strategically the best to process next. We present a new constrained softmax transformation that ensures the same cumulative attention to every word, and show how to efficiently evaluate and backpropagate over it. Our models compare favourably to BILSTM taggers on three sequence tagging tasks.

null

10.18653/v1/D17-1036

null

57,524

inproceedings

kaji-kobayashi-2017-incremental

Incremental Skip-gram Model with Negative Sampling

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1037/

Kaji, Nobuhiro and Kobayashi, Hayato

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

363--371

This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives. Existing methods of neural word embeddings, including SGNS, are multi-pass algorithms and thus cannot perform incremental model update. To address this problem, we present a simple incremental extension of SGNS and provide a thorough theoretical analysis to demonstrate its validity. Empirical experiments demonstrated the correctness of the theoretical analysis as well as the practical usefulness of the incremental algorithm.

null

10.18653/v1/D17-1037

null

57,525

inproceedings

ruder-plank-2017-learning

Learning to select data for transfer learning with {B}ayesian Optimization

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1038/

Ruder, Sebastian and Plank, Barbara

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

372--382

Domain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable for respective tasks. Inspired by work on curriculum learning, we propose to learn data selection measures using Bayesian Optimization and evaluate them across models, domains and tasks. Our learned measures outperform existing domain similarity measures significantly on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We show the importance of complementing similarity with diversity, and that learned measures are{--}to some degree{--}transferable across models, domains, and even tasks.

null

10.18653/v1/D17-1038

null

57,526

inproceedings

ramachandran-etal-2017-unsupervised

Unsupervised Pretraining for Sequence to Sequence Learning

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1039/

Ramachandran, Prajit and Liu, Peter and Le, Quoc

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

383--391

This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two language models and then fine-tuned with labeled data. We apply this method to challenging benchmarks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main result is that pretraining improves the generalization of seq2seq models. We achieve state-of-the-art results on the WMT English{\textrightarrow}German task, surpassing a range of methods using both phrase-based machine translation and neural machine translation. Our method achieves a significant improvement of 1.3 BLEU from th previous best models on both WMT`14 and WMT`15 English{\textrightarrow}German. We also conduct human evaluations on abstractive summarization and find that our method outperforms a purely supervised learning baseline in a statistically significant manner.

null

10.18653/v1/D17-1039

null

57,527

inproceedings

britz-etal-2017-efficient

Efficient Attention using a Fixed-Size Memory Representation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1040/

Britz, Denny and Guan, Melody and Luong, Minh-Thang

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

392--400

The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is more efficient. Our technique predicts a compact set of K attention contexts during encoding and lets the decoder compute an efficient lookup that does not need to consult the memory. We show that our approach performs on-par with the standard attention mechanism while yielding inference speedups of 20{\%} for real-world translation tasks and more for tasks with longer sequences. By visualizing attention scores we demonstrate that our models learn distinct, meaningful alignments.

null

10.18653/v1/D17-1040

null

57,528

inproceedings

park-etal-2017-rotated

Rotated Word Vector Representations and their Interpretability

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1041/

Park, Sungjoon and Bak, JinYeong and Oh, Alice

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

401--411

Vector representation of words improves performance in various NLP tasks, but the high dimensional word vectors are very difficult to interpret. We apply several rotation algorithms to the vector representation of words to improve the interpretability. Unlike previous approaches that induce sparsity, the rotated vectors are interpretable while preserving the expressive performance of the original vectors. Furthermore, any prebuilt word vector representation can be rotated for improved interpretability. We apply rotation to skipgrams and glove and compare the expressive power and interpretability with the original vectors and the sparse overcomplete vectors. The results show that the rotated vectors outperform the original and the sparse overcomplete vectors for interpretability and expressiveness tasks.

null

10.18653/v1/D17-1041

null

57,529

inproceedings

alvarez-melis-jaakkola-2017-causal

A causal framework for explaining the predictions of black-box sequence-to-sequence models

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1042/

Alvarez-Melis, David and Jaakkola, Tommi

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

412--421

We interpret the predictions of any black-box structured input-structured output model around a specific input-output pair. Our method returns an {\textquotedblleft}explanation{\textquotedblright} consisting of groups of input-output tokens that are causally related. These dependencies are inferred by querying the model with perturbed inputs, generating a graph over tokens from the responses, and solving a partitioning problem to select the most relevant components. We focus the general approach on sequence-to-sequence problems, adopting a variational autoencoder to yield meaningful input perturbations. We test our method across several NLP sequence generation tasks.

null

10.18653/v1/D17-1042

null

57,530

inproceedings

lavergne-yvon-2017-learning

Learning the Structure of Variable-Order {CRF}s: a finite-state perspective

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1044/

Lavergne, Thomas and Yvon, Fran{\c{c}}ois

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

433--439

The computational complexity of linear-chain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies. Such situations are not rare and arise when dealing with morphologically rich languages or joint labelling tasks. We extend here recent proposals to consider variable order CRFs. Using an effective finite-state representation of variable-length dependencies, we propose new ways to perform feature selection at large scale and report experimental results where we outperform strong baselines on a tagging task.

null

10.18653/v1/D17-1044

null

57,532

inproceedings

aji-heafield-2017-sparse

Sparse Communication for Distributed Gradient Descent

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1045/

Aji, Alham Fikri and Heafield, Kenneth

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

440--445

We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense updates. Gradient updates are positively skewed as most updates are near zero, so we map the 99{\%} smallest updates (by absolute value) to zero then exchange sparse matrices. This method can be combined with quantization to further improve the compression. We explore different configurations and apply them to neural machine translation and MNIST image classification tasks. Most configurations work on MNIST, whereas different configurations reduce convergence rate on the more complex translation task. Our experiments show that we can achieve up to 49{\%} speed up on MNIST and 22{\%} on NMT without damaging the final accuracy or BLEU.

null

10.18653/v1/D17-1045

null

57,533

inproceedings

lu-etal-2017-adagrad

Why {ADAGRAD} Fails for Online Topic Modeling

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1046/

Lu, You and Lund, Jeffrey and Boyd-Graber, Jordan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

446--451

Online topic modeling, i.e., topic modeling with stochastic variational inference, is a powerful and efficient technique for analyzing large datasets, and ADAGRAD is a widely-used technique for tuning learning rates during online gradient optimization. However, these two techniques do not work well together. We show that this is because ADAGRAD uses accumulation of previous gradients as the learning rates' denominators. For online topic modeling, the magnitude of gradients is very large. It causes learning rates to shrink very quickly, so the parameters cannot fully converge until the training ends

null

10.18653/v1/D17-1046

null

57,534

inproceedings

chen-etal-2017-recurrent

Recurrent Attention Network on Memory for Aspect Sentiment Analysis

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1047/

Chen, Peng and Sun, Zhongqian and Bing, Lidong and Yang, Wei

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

452--461

We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information. The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthens the expressive power of our model for handling more complications. The weighted-memory mechanism not only helps us avoid the labor-intensive feature engineering work, but also provides a tailor-made memory for different opinion targets of a sentence. We examine the merit of our model on four datasets: two are from SemEval2014, i.e. reviews of restaurants and laptops; a twitter dataset, for testing its performance on social media data; and a Chinese news comment dataset, for testing its language sensitivity. The experimental results show that our model consistently outperforms the state-of-the-art methods on different types of data.

null

10.18653/v1/D17-1047

null

57,535

inproceedings

long-etal-2017-cognition

A Cognition Based Attention Model for Sentiment Analysis

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1048/

Long, Yunfei and Lu, Qin and Xiang, Rong and Li, Minglei and Huang, Chu-Ren

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

462--471

Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.

null

10.18653/v1/D17-1048

null

57,536

inproceedings

poddar-etal-2017-author

Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1049/

Poddar, Lahari and Hsu, Wynne and Lee, Mong Li

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

472--481

User generated content about products and services in the form of reviews are often diverse and even contradictory. This makes it difficult for users to know if an opinion in a review is prevalent or biased. We study the problem of searching for supporting opinions in the context of reviews. We propose a framework called SURF, that first identifies opinions expressed in a review, and then finds similar opinions from other reviews. We design a novel probabilistic graphical model that captures opinions as a combination of aspect, topic and sentiment dimensions, takes into account the preferences of individual authors, as well as the quality of the entity under review, and encodes the flow of thoughts in a review by constraining the aspect distribution dynamically among successive review segments. We derive a similarity measure that considers both lexical and semantic similarity to find supporting opinions. Experiments on TripAdvisor hotel reviews and Yelp restaurant reviews show that our model outperforms existing methods for modeling opinions, and the proposed framework is effective in finding supporting opinions.

null

10.18653/v1/D17-1049

null

57,537

inproceedings

ghosh-veale-2017-magnets

Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1050/

Ghosh, Aniruddha and Veale, Tony

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

482--491

Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meaning, affect and attitude. Concision requires wit to produce and wit to understand, which demands from each party knowledge of norms, context and a speaker`s mindset. Insight into a speaker`s psychological profile at the time of production is a valuable source of context for sarcasm detection. Using a neural architecture, we show significant gains in detection accuracy when knowledge of the speaker`s mood at the time of production can be inferred. Our focus is on sarcasm detection on Twitter, and show that the mood exhibited by a speaker over tweets leading up to a new post is as useful a cue for sarcasm as the topical context of the post itself. The work opens the door to an empirical exploration not just of sarcasm in text but of the sarcastic state of mind.

null

10.18653/v1/D17-1050

null

57,538

inproceedings

morales-zhai-2017-identifying

Identifying Humor in Reviews using Background Text Sources

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1051/

Morales, Alex and Zhai, Chengxiang

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

492--501

We study the problem of automatically identifying humorous text from a new kind of text data, i.e., online reviews. We propose a generative language model, based on the theory of incongruity, to model humorous text, which allows us to leverage background text sources, such as Wikipedia entry descriptions, and enables construction of multiple features for identifying humorous reviews. Evaluation of these features using supervised learning for classifying reviews into humorous and non-humorous reviews shows that the features constructed based on the proposed generative model are much more effective than the major features proposed in the existing literature, allowing us to achieve almost 86{\%} accuracy. These humorous review predictions can also supply good indicators for identifying helpful reviews.

null

10.18653/v1/D17-1051

null

57,539

inproceedings

wang-xia-2017-sentiment

Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment Supervision

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1052/

Wang, Leyi and Xia, Rui

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

502--510

Sentiment lexicon is an important tool for identifying the sentiment polarity of words and texts. How to automatically construct sentiment lexicons has become a research topic in the field of sentiment analysis and opinion mining. Recently there were some attempts to employ representation learning algorithms to construct a sentiment lexicon with sentiment-aware word embedding. However, these methods were normally trained under document-level sentiment supervision. In this paper, we develop a neural architecture to train a sentiment-aware word embedding by integrating the sentiment supervision at both document and word levels, to enhance the quality of word embedding as well as the sentiment lexicon. Experiments on the SemEval 2013-2016 datasets indicate that the sentiment lexicon generated by our approach achieves the state-of-the-art performance in both supervised and unsupervised sentiment classification, in comparison with several strong sentiment lexicon construction methods.

null

10.18653/v1/D17-1052

null

57,540

inproceedings

xu-wan-2017-towards

Towards a Universal Sentiment Classifier in Multiple languages

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1053/

Xu, Kui and Wan, Xiaojun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

511--520

Existing sentiment classifiers usually work for only one specific language, and different classification models are used in different languages. In this paper we aim to build a universal sentiment classifier with a single classification model in multiple different languages. In order to achieve this goal, we propose to learn multilingual sentiment-aware word embeddings simultaneously based only on the labeled reviews in English and unlabeled parallel data available in a few language pairs. It is not required that the parallel data exist between English and any other language, because the sentiment information can be transferred into any language via pivot languages. We present the evaluation results of our universal sentiment classifier in five languages, and the results are very promising even when the parallel data between English and the target languages are not used. Furthermore, the universal single classifier is compared with a few cross-language sentiment classifiers relying on direct parallel data between the source and target languages, and the results show that the performance of our universal sentiment classifier is very promising compared to that of different cross-language classifiers in multiple target languages.

null

10.18653/v1/D17-1053

null

57,541

inproceedings

dou-2017-capturing

Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1054/

Dou, Zi-Yi

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

521--526

Document-level sentiment classification is a fundamental problem which aims to predict a user`s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the sentiment and products which are evaluated. To address the issue, we propose a deep memory network for document-level sentiment classification which could capture the user and product information at the same time. To prove the effectiveness of our algorithm, we conduct experiments on IMDB and Yelp datasets and the results indicate that our model can achieve better performance than several existing methods.

null

10.18653/v1/D17-1054

null

57,542

inproceedings

yang-etal-2017-identifying

Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1055/

Yang, Min and Mei, Jincheng and Ji, Heng and Zhao, Wei and Zhao, Zhou and Chen, Xiaojun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

527--533

We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information. The experimental results demonstrate that LDST performs very well at discovering topics and sentiments from social media and tracking their shifts in different geographical regions during emergencies and disasters. We will release the data and source code after this work is published.

null

10.18653/v1/D17-1055

null

57,543

inproceedings

yu-etal-2017-refining

Refining Word Embeddings for Sentiment Analysis

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1056/

Yu, Liang-Chih and Wang, Jin and Lai, K. Robert and Zhang, Xuejie

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

534--539

Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically fail to capture sufficient sentiment information. This may result in words with similar vector representations having an opposite sentiment polarity (e.g., good and bad), thus degrading sentiment analysis performance. Therefore, this study proposes a word vector refinement model that can be applied to any pre-trained word vectors (e.g., Word2vec and GloVe). The refinement model is based on adjusting the vector representations of words such that they can be closer to both semantically and sentimentally similar words and further away from sentimentally dissimilar words. Experimental results show that the proposed method can improve conventional word embeddings and outperform previously proposed sentiment embeddings for both binary and fine-grained classification on Stanford Sentiment Treebank (SST).

null

10.18653/v1/D17-1056

null

57,544

inproceedings

akhtar-etal-2017-multilayer

A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1057/

Akhtar, Md Shad and Kumar, Abhishek and Ghosal, Deepanway and Ekbal, Asif and Bhattacharyya, Pushpak

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

540--546

In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis. We develop various deep learning models based on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). These are trained on top of pre-trained, autoencoder-based, financial word embeddings and lexicon features. An ensemble is constructed by combining these deep learning models and a classical supervised model based on Support Vector Regression (SVR). We evaluate our proposed technique on a benchmark dataset of SemEval-2017 shared task on financial sentiment analysis. The propose model shows impressive results on two datasets, i.e. microblogs and news headlines datasets. Comparisons show that our proposed model performs better than the existing state-of-the-art systems for the above two datasets by 2.0 and 4.1 cosine points, respectively.

null

10.18653/v1/D17-1057

null

57,545

inproceedings

sharma-etal-2017-sentiment

Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1058/

Sharma, Raksha and Somani, Arpan and Kumar, Lakshya and Bhattacharyya, Pushpak

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

547--552

Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis. For example, {\textquoteleft}master', {\textquoteleft}seasoned' and {\textquoteleft}familiar' point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semi-supervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman`s rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.

null

10.18653/v1/D17-1058

null

57,546

inproceedings

wang-etal-2017-sentiment

Sentiment Lexicon Expansion Based on Neural {PU} Learning, Double Dictionary Lookup, and Polarity Association

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1059/

Wang, Yasheng and Zhang, Yang and Liu, Bing

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

553--563

Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words in social media. This prompted us to make a new attempt to study sentiment lexicon expansion. This paper first poses the problem as a PU learning problem, which is a new formulation. It then proposes a new PU learning method suitable for our problem using a neural network. The results are enhanced further with a new dictionary-based technique and a novel polarity classification technique. Experimental results show that the proposed approach outperforms baseline methods greatly.

null

10.18653/v1/D17-1059

null

57,547

inproceedings

xiong-etal-2017-deeppath

{D}eep{P}ath: A Reinforcement Learning Method for Knowledge Graph Reasoning

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1060/

Xiong, Wenhan and Hoang, Thien and Wang, William Yang

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

564--573

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector-space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.

null

10.18653/v1/D17-1060

null

57,548

inproceedings

nogueira-cho-2017-task

Task-Oriented Query Reformulation with Reinforcement Learning

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1061/

Nogueira, Rodrigo and Cho, Kyunghyun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

574--583

Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20{\%} in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.

null

10.18653/v1/D17-1061

null

57,549

inproceedings

zhang-lapata-2017-sentence

Sentence Simplification with Deep Reinforcement Learning

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1062/

Zhang, Xingxing and Lapata, Mirella

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

584--594

Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences. We address the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework. Our model, which we call DRESS (as shorthand for \textbf{D}eep \textbf{RE}inforcement \textbf{S}entence \textbf{S}implification), explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input. Experiments on three datasets demonstrate that our model outperforms competitive simplification systems.

null

10.18653/v1/D17-1062

null

57,550

inproceedings

fang-etal-2017-learning

Learning how to Active Learn: A Deep Reinforcement Learning Approach

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1063/

Fang, Meng and Li, Yuan and Cohn, Trevor

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

595--605

Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address these shortcomings, we introduce a novel formulation by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the active learning heuristic. Importantly, our method allows the selection policy learned using simulation to one language to be transferred to other languages. We demonstrate our method using cross-lingual named entity recognition, observing uniform improvements over traditional active learning algorithms.

null

10.18653/v1/D17-1063

null

57,551

inproceedings

narayan-etal-2017-split

Split and Rephrase

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1064/

Narayan, Shashi and Gardent, Claire and Cohen, Shay B. and Shimorina, Anastasia

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

606--616

We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labellers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning. Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.

null

10.18653/v1/D17-1064

null

57,552

inproceedings

xu-etal-2017-neural

Neural Response Generation via {GAN} with an Approximate Embedding Layer

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1065/

Xu, Zhen and Liu, Bingquan and Wang, Baoxun and Sun, Chengjie and Wang, Xiaolong and Wang, Zhuoran and Qi, Chao

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

617--626

This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones. In addition, the proposed method introduces an approximate embedding layer to solve the non-differentiable problem caused by the sampling-based output decoding procedure in the Seq2Seq generative model. The GAN setup provides an effective way to avoid noninformative responses (a.k.a {\textquotedblleft}safe responses{\textquotedblright}), which are frequently observed in traditional neural response generators. The experimental results show that the proposed approach significantly outperforms existing neural response generation models in diversity metrics, with slight increases in relevance scores as well, when evaluated on both a Mandarin corpus and an English corpus.

null

10.18653/v1/D17-1065

null

57,553

inproceedings

semeniuta-etal-2017-hybrid

A Hybrid Convolutional Variational Autoencoder for Text Generation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1066/

Semeniuta, Stanislau and Severyn, Aliaksei and Barth, Erhardt

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

627--637

In this paper we explore the effect of architectural choices on learning a variational autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and deconvolutional components with a recurrent language model. Our architecture exhibits several attractive properties such as faster run time and convergence, ability to better handle long sequences and, more importantly, it helps to avoid the issue of the VAE collapsing to a deterministic model.

null

10.18653/v1/D17-1066

null

57,554

inproceedings

hossain-etal-2017-filling

Filling the Blanks (hint: plural noun) for Mad {L}ibs Humor

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1067/

Hossain, Nabil and Krumm, John and Vanderwende, Lucy and Horvitz, Eric and Kautz, Henry

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

638--647

Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny.

null

10.18653/v1/D17-1067

null

57,555

inproceedings

santus-etal-2017-measuring

Measuring Thematic Fit with Distributional Feature Overlap

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1068/

Santus, Enrico and Chersoni, Emmanuele and Lenci, Alessandro and Blache, Philippe

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

648--658

In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensions of typical role fillers), and then we compute thematic fit as a weighted overlap between the top features of candidate fillers and role prototypes. Our experiments show that our method consistently outperforms a baseline re-implementing a state-of-the-art system, and achieves better or comparable results to those reported in the literature for the other unsupervised systems. Moreover, it provides an explicit representation of the features characterizing verb-specific semantic roles.

null

10.18653/v1/D17-1068

null

57,556

inproceedings

mekala-etal-2017-scdv

{SCDV} : Sparse Composite Document Vectors using soft clustering over distributional representations

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1069/

Mekala, Dheeraj and Gupta, Vivek and Paranjape, Bhargavi and Karnick, Harish

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

659--669

We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embeddings are clustered to capture multiple semantic contexts in which words occur. They are then chained together to form document topic-vectors that can express complex, multi-topic documents. Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG. We also show that SCDV embeddings perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval. Moreover, we achieve a significant reduction in training and prediction times compared to other representation methods. SCDV achieves best of both worlds - better performance with lower time and space complexity.

null

10.18653/v1/D17-1069

null

57,557

inproceedings

conneau-etal-2017-supervised

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1070/

Conneau, Alexis and Kiela, Douwe and Schwenk, Holger and Barrault, Lo{\"ic and Bordes, Antoine

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

670--680

Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available.

null

10.18653/v1/D17-1070

null

57,558

inproceedings

yanaka-etal-2017-determining

Determining Semantic Textual Similarity using Natural Deduction Proofs

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1071/

Yanaka, Hitomi and Mineshima, Koji and Mart{\'i}nez-G{\'o}mez, Pascual and Bekki, Daisuke

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

681--691

Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higher-order automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logic-based systems and that features derived from the proofs are effective for learning textual similarity.

null

10.18653/v1/D17-1071

null

57,559

inproceedings

gong-etal-2017-multi

Multi-Grained {C}hinese Word Segmentation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1072/

Gong, Chen and Li, Zhenghua and Zhang, Min and Jiang, Xinzhou

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

692--703

Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence. However, Sproat et al. (1997) show that the inter-native-speaker consistency ratio over Chinese word boundaries is only 76{\%}, indicating single-grained WS (SWS) imposes unnecessary challenges on both manual annotation and statistical modeling. Moreover, WS results of different granularities can be complementary and beneficial for high-level applications. This work proposes and addresses multi-grained WS (MWS). We build a large-scale pseudo MWS dataset for model training and tuning by leveraging the annotation heterogeneity of three SWS datasets. Then we manually annotate 1,500 test sentences with true MWS annotations. Finally, we propose three benchmark approaches by casting MWS as constituent parsing and sequence labeling. Experiments and analysis lead to many interesting findings.

null

10.18653/v1/D17-1072

null

57,560

inproceedings

zalmout-habash-2017-dont

Don`t Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for {A}rabic

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1073/

Zalmout, Nasser and Habash, Nizar

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

704--713

This paper presents a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several configurations and embedding levels to model the various morphological features. Our experiments show that these models outperform state-of-the-art systems without explicit use of feature engineering. However, adding learning features from a morphological analyzer to model the space of possible analyses provides additional improvement. We make use of the resulting morphological models for scoring and ranking the analyses of the morphological analyzer for morphological disambiguation. The results show significant gains in accuracy across several evaluation metrics. Our system results in 4.4{\%} absolute increase over the state-of-the-art in full morphological analysis accuracy (30.6{\%} relative error reduction), and 10.6{\%} (31.5{\%} relative error reduction) for out-of-vocabulary words.

null

10.18653/v1/D17-1073

null

57,561

inproceedings

cotterell-etal-2017-paradigm

Paradigm Completion for Derivational Morphology

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1074/

Cotterell, Ryan and Vylomova, Ekaterina and Khayrallah, Huda and Kirov, Christo and Yarowsky, David

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

714--720

The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4{\%}. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.

null

10.18653/v1/D17-1074

null

57,562

inproceedings

stratos-2017-sub

A Sub-Character Architecture for {K}orean Language Processing

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1075/

Stratos, Karl

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

721--726

We introduce a novel sub-character architecture that exploits a unique compositional structure of the Korean language. Our method decomposes each character into a small set of primitive phonetic units called jamo letters from which character- and word-level representations are induced. The jamo letters divulge syntactic and semantic information that is difficult to access with conventional character-level units. They greatly alleviate the data sparsity problem, reducing the observation space to 1.6{\%} of the original while increasing accuracy in our experiments. We apply our architecture to dependency parsing and achieve dramatic improvement over strong lexical baselines.

null

10.18653/v1/D17-1075

null

57,563

inproceedings

horsmann-zesch-2017-lstms

Do {LSTM}s really work so well for {P}o{S} tagging? {--} A replication study

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1076/

Horsmann, Tobias and Zesch, Torsten

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

727--736

A recent study by Plank et al. (2016) found that LSTM-based PoS taggers considerably improve over the current state-of-the-art when evaluated on the corpora of the Universal Dependencies project that use a coarse-grained tagset. We replicate this study using a fresh collection of 27 corpora of 21 languages that are annotated with fine-grained tagsets of varying size. Our replication confirms the result in general, and we additionally find that the advantage of LSTMs is even bigger for larger tagsets. However, we also find that for the very large tagsets of morphologically rich languages, hand-crafted morphological lexicons are still necessary to reach state-of-the-art performance.

null

10.18653/v1/D17-1076

null

57,564

inproceedings

mcconnaughey-etal-2017-labeled

The Labeled Segmentation of Printed Books

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1077/

McConnaughey, Lara and Dai, Jennifer and Bamman, David

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

737--747

We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as Table of Contents, Preface, Index) to the document structure of printed books. We manually annotate the page-level structural categories for a large dataset totaling 294,816 pages in 1,055 books evenly sampled from 1750-1922, and present empirical results comparing the performance of several classes of models. The best-performing model, a bidirectional LSTM with rich features, achieves an overall accuracy of 95.8 and a class-balanced macro F-score of 71.4.

null

10.18653/v1/D17-1077

null

57,565

inproceedings

cotterell-heigold-2017-cross

Cross-lingual Character-Level Neural Morphological Tagging

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1078/

Cotterell, Ryan and Heigold, Georg

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

748--759

Even for common NLP tasks, sufficient supervision is not available in many languages {--} morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones.

null

10.18653/v1/D17-1078

null

57,566

inproceedings

oshikiri-2017-segmentation

Segmentation-Free Word Embedding for Unsegmented Languages

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1080/

Oshikiri, Takamasa

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

767--772

In this paper, we propose a new pipeline of word embedding for unsegmented languages, called segmentation-free word embedding, which does not require word segmentation as a preprocessing step. Unlike space-delimited languages, unsegmented languages, such as Chinese and Japanese, require word segmentation as a preprocessing step. However, word segmentation, that often requires manually annotated resources, is difficult and expensive, and unavoidable errors in word segmentation affect downstream tasks. To avoid these problems in learning word vectors of unsegmented languages, we consider word co-occurrence statistics over all possible candidates of segmentations based on frequent character n-grams instead of segmented sentences provided by conventional word segmenters. Our experiments of noun category prediction tasks on raw Twitter, Weibo, and Wikipedia corpora show that the proposed method outperforms the conventional approaches that require word segmenters.

null

10.18653/v1/D17-1080

null

57,568

inproceedings

sachan-etal-2017-textbooks

From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1081/

Sachan, Mrinmaya and Dubey, Kumar and Xing, Eric

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

773--784

Textbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured axiomatic knowledge from math textbooks. Our approach uses rich contextual and typographical features extracted from raw textbooks. It leverages the redundancy and shared ordering across multiple textbooks to further refine the harvested axioms. These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems.

null

10.18653/v1/D17-1081

null

57,569

inproceedings

lai-etal-2017-race

{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1082/

Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

785--794

We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students' ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43{\%}) and the ceiling human performance (95{\%}). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at \url{http://www.cs.cmu.edu/~glai1/data/race/}and the code is available at \url{https://github.com/qizhex/RACE_AR_baselines}.

null

10.18653/v1/D17-1082

null

57,570

inproceedings

hopkins-etal-2017-beyond

Beyond Sentential Semantic Parsing: Tackling the Math {SAT} with a Cascade of Tree Transducers

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1083/

Hopkins, Mark and Petrescu-Prahova, Cristian and Levin, Roie and Le Bras, Ronan and Herrasti, Alvaro and Joshi, Vidur

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

795--804

We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions {--} the math portion of the Scholastic Aptitude Test (SAT). By using a tree transducer cascade as its basic architecture, our system propagates uncertainty from multiple sources (e.g. coreference resolution or verb interpretation) until it can be confidently resolved. Experiments show the first-ever results 43{\%} recall and 91{\%} precision) on SAT algebra word problems. We also apply our system to the public Dolphin algebra question set, and improve the state-of-the-art F1-score from 73.9{\%} to 77.0{\%}.

null

10.18653/v1/D17-1083

null

57,571

inproceedings

huang-etal-2017-learning

Learning Fine-Grained Expressions to Solve Math Word Problems

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1084/

Huang, Danqing and Shi, Shuming and Lin, Chin-Yew and Yin, Jian

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

805--814

This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4{\%} on the linear Dolphin18K benchmark, which is 10{\%} (54{\%} relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12{\%} (59{\%} relative) on the TS6 benchmark subset.

null

10.18653/v1/D17-1084

null

57,572

inproceedings

liu-etal-2017-structural

Structural Embedding of Syntactic Trees for Machine Comprehension

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1085/

Liu, Rui and Hu, Junjie and Wei, Wei and Yang, Zi and Nyberg, Eric

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

815--824

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.

null

10.18653/v1/D17-1085

null

57,573

inproceedings

long-etal-2017-world

World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical {LSTM}s Using External Descriptions

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1086/

Long, Teng and Bengio, Emmanuel and Lowe, Ryan and Cheung, Jackie Chi Kit and Precup, Doina

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

825--834

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.

null

10.18653/v1/D17-1086

null

57,574

inproceedings

golub-etal-2017-two

Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1087/

Golub, David and Huang, Po-Sen and He, Xiaodong and Deng, Li

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

835--844

We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed synthesis network with a pretrained model on the SQuAD dataset, we achieve an F1 measure of 46.6{\%} on the challenging NewsQA dataset, approaching performance of in-domain models (F1 measure of 50.0{\%}) and outperforming the out-of-domain baseline by 7.6{\%}, without use of provided annotations.

null

10.18653/v1/D17-1087

null

57,575

inproceedings

wang-etal-2017-deep

Deep Neural Solver for Math Word Problems

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1088/

Wang, Yan and Liu, Xiaojiang and Shi, Shuming

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

845--854

This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) model, without sophisticated feature engineering. We further design a hybrid model that combines the RNN model and a similarity-based retrieval model to achieve additional performance improvement. Experiments conducted on a large dataset show that the RNN model and the hybrid model significantly outperform state-of-the-art statistical learning methods for math word problem solving.

null

10.18653/v1/D17-1088

null

57,576

inproceedings

p-etal-2017-latent

Latent Space Embedding for Retrieval in Question-Answer Archives

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1089/

P, Deepak and Garg, Dinesh and Shevade, Shirish

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

855--865

Community-driven Question Answering (CQA) systems such as Yahoo! Answers have become valuable sources of reusable information. CQA retrieval enables usage of historical CQA archives to solve new questions posed by users. This task has received much recent attention, with methods building upon literature from translation models, topic models, and deep learning. In this paper, we devise a CQA retrieval technique, LASER-QA, that embeds question-answer pairs within a unified latent space preserving the local neighborhood structure of question and answer spaces. The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval. Through an empirical analysis on various real-world QA datasets, we illustrate the improved effectiveness of LASER-QA over state-of-the-art methods.

null

10.18653/v1/D17-1089

null

57,577

inproceedings

duan-etal-2017-question

Question Generation for Question Answering

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1090/

Duan, Nan and Tang, Duyu and Chen, Peng and Zhou, Ming

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

866--874

This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems. We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.

null

10.18653/v1/D17-1090

null

57,578

inproceedings

dong-etal-2017-learning

Learning to Paraphrase for Question Answering

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1091/

Dong, Li and Mallinson, Jonathan and Reddy, Siva and Lapata, Mirella

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

875--886

Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-to-end using question-answer pairs as a supervision signal. A question and its paraphrases serve as input to a neural scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers. We evaluate our approach on QA over Freebase and answer sentence selection. Experimental results on three datasets show that our framework consistently improves performance, achieving competitive results despite the use of simple QA models.

null

10.18653/v1/D17-1091

null

57,579

inproceedings

meng-etal-2017-temporal

Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an {LSTM}-based Architecture

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1092/

Meng, Yuanliang and Rumshisky, Anna and Romanov, Alexey

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

887--896

In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relations. A {\textquotedblleft}double-checking{\textquotedblright} technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes. An efficient pruning algorithm resolves conflicts globally. Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-of-the-art methods by a large margin. We also conduct intrinsic evaluation and post state-of-the-art results on Timebank-Dense.

null

10.18653/v1/D17-1092

null

57,580

inproceedings

tymoshenko-etal-2017-ranking

Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1093/

Tymoshenko, Kateryna and Bonadiman, Daniele and Moschitti, Alessandro

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

897--902

Recent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector Machines (SVMs) is promising as it can exploit both the syntactic patterns captured by TKs and the embeddings learned by CNNs. However, the embeddings are constructed according to a classification function, which is not directly exploitable in the preference ranking algorithm of SVMs. In this work, we propose a new hybrid approach combining preference ranking applied to TKs and pointwise ranking applied to CNNs. We show that our approach produces better results on two well-known and rather different datasets: WikiQA for answer sentence selection and SemEval cQA for comment selection in Community Question Answering.

null

10.18653/v1/D17-1093

null

57,581

inproceedings

yavuz-etal-2017-recovering

Recovering Question Answering Errors via Query Revision

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1094/

Yavuz, Semih and Gur, Izzeddin and Su, Yu and Yan, Xifeng

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

903--909

The existing factoid QA systems often lack a post-inspection component that can help models recover from their own mistakes. In this work, we propose to crosscheck the corresponding KB relations behind the predicted answers and identify potential inconsistencies. Instead of developing a new model that accepts evidences collected from these relations, we choose to plug them back to the original questions directly and check if the revised question makes sense or not. A bidirectional LSTM is applied to encode revised questions. We develop a scoring mechanism over the revised question encodings to refine the predictions of a base QA system. This approach can improve the F1 score of STAGG (Yih et al., 2015), one of the leading QA systems, from 52.5{\%} to 53.9{\%} on WEBQUESTIONS data.

null

10.18653/v1/D17-1094

null

57,582

inproceedings

delbrouck-dupont-2017-empirical

An empirical study on the effectiveness of images in Multimodal Neural Machine Translation

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1095/

Delbrouck, Jean-Benoit and Dupont, St{\'e}phane

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

910--919

In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effectiveness of the attention mechanism has also been explored for multi-modal tasks, where it becomes possible to focus both on sentence parts and image regions that they describe. In this paper, we compare several attention mechanism on the multi-modal translation task (English, image {\textrightarrow} German) and evaluate the ability of the model to make use of images to improve translation. We surpass state-of-the-art scores on the Multi30k data set, we nevertheless identify and report different misbehavior of the machine while translating.

null

10.18653/v1/D17-1095

null

57,583

inproceedings

vijayakumar-etal-2017-sound

Sound-{W}ord2{V}ec: Learning Word Representations Grounded in Sounds

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1096/

Vijayakumar, Ashwin and Vedantam, Ramakrishna and Parikh, Devi

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

920--925

To be able to interact better with humans, it is crucial for machines to understand sound {--} a primary modality of human perception. Previous works have used sound to learn embeddings for improved generic semantic similarity assessment. In this work, we treat sound as a first-class citizen, studying downstream 6textual tasks which require aural grounding. To this end, we propose sound-word2vec {--} a new embedding scheme that learns specialized word embeddings grounded in sounds. For example, we learn that two seemingly (semantically) unrelated concepts, like leaves and paper are similar due to the similar rustling sounds they make. Our embeddings prove useful in textual tasks requiring aural reasoning like text-based sound retrieval and discovering Foley sound effects (used in movies). Moreover, our embedding space captures interesting dependencies between words and onomatopoeia and outperforms prior work on aurally-relevant word relatedness datasets such as AMEN and ASLex.

null

10.18653/v1/D17-1096

null

57,584

inproceedings

mahendru-etal-2017-promise

The Promise of Premise: Harnessing Question Premises in Visual Question Answering

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1097/

Mahendru, Aroma and Prabhu, Viraj and Mohapatra, Akrit and Batra, Dhruv and Lee, Stefan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

926--935

In this paper, we make a simple observation that questions about images often contain premises {--} objects and relationships implied by the question {--} and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is false (i.e. not depicted in the image). We leverage this observation to construct a dataset for Question Relevance Prediction and Explanation (QRPE) by searching for false premises. We train novel question relevance detection models and show that models that reason about premises consistently outperform models that do not. We also find that forcing standard VQA models to reason about premises during training can lead to improvements on tasks requiring compositional reasoning.

null

10.18653/v1/D17-1097

null

57,585

inproceedings

anderson-etal-2017-guided

Guided Open Vocabulary Image Captioning with Constrained Beam Search

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1098/

Anderson, Peter and Fernando, Basura and Johnson, Mark and Gould, Stephen

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

936--945

Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels.

null

10.18653/v1/D17-1098

null

57,586

inproceedings

zellers-choi-2017-zero

Zero-Shot Activity Recognition with Verb Attribute Induction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1099/

Zellers, Rowan and Choi, Yejin

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

946--958

In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb {\textquotedblleft}salute{\textquotedblright} has several properties, such as being a light movement, a social act, and short in duration. We use these attributes as the internal mapping between visual and textual representations to reason about a previously unseen action. In contrast to much prior work that assumes access to gold standard attributes for zero-shot classes and focuses primarily on object attributes, our model uniquely learns to infer action attributes from dictionary definitions and distributed word representations. Experimental results confirm that action attributes inferred from language can provide a predictive signal for zero-shot prediction of previously unseen activities.

null

10.18653/v1/D17-1099

null

57,587

inproceedings

zarriess-schlangen-2017-deriving

Deriving continous grounded meaning representations from referentially structured multimodal contexts

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1100/

Zarrie{\ss}, Sina and Schlangen, David

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

959--965

Corpora of referring expressions paired with their visual referents are a good source for learning word meanings directly grounded in visual representations. Here, we explore additional ways of extracting from them word representations linked to multi-modal context: through expressions that refer to the same object, and through expressions that refer to different objects in the same scene. We show that continuous meaning representations derived from these contexts capture complementary aspects of similarity, , even if not outperforming textual embeddings trained on very large amounts of raw text when tested on standard similarity benchmarks. We propose a new task for evaluating grounded meaning representations{---}detection of potentially co-referential phrases{---}and show that it requires precise denotational representations of attribute meanings, which our method provides.

null

10.18653/v1/D17-1100

null

57,588

inproceedings

yu-etal-2017-hierarchically

Hierarchically-Attentive {RNN} for Album Summarization and Storytelling

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1101/

Yu, Licheng and Bansal, Mohit and Berg, Tamara

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

966--971

We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.

null

10.18653/v1/D17-1101

null

57,589

inproceedings

fu-etal-2017-video

Video Highlight Prediction Using Audience Chat Reactions

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1102/

Fu, Cheng-Yang and Lee, Joon and Bansal, Mohit and Berg, Alexander

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

972--978

Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis. We present methods addressing the problem of automatic video highlight prediction based on joint visual features and textual analysis of the real-world audience discourse with complex slang, in both English and traditional Chinese. We present a novel dataset based on League of Legends championships recorded from North American and Taiwanese Twitch.tv channels (will be released for further research), and demonstrate strong results on these using multimodal, character-level CNN-RNN model architectures.

null

10.18653/v1/D17-1102

null

57,590

inproceedings

pasunuru-bansal-2017-reinforced

Reinforced Video Captioning with Entailment Rewards

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1103/

Pasunuru, Ramakanth and Bansal, Mohit

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

979--985

Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model. Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.

null

10.18653/v1/D17-1103

null

57,591

inproceedings

mu-etal-2017-evaluating

Evaluating Hierarchies of Verb Argument Structure with Hierarchical Clustering

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1104/

Mu, Jesse and Hartshorne, Joshua K. and O{'}Donnell, Timothy

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

986--991

Verbs can only be used with a few specific arrangements of their arguments (syntactic frames). Most theorists note that verbs can be organized into a hierarchy of verb classes based on the frames they admit. Here we show that such a hierarchy is objectively well-supported by the patterns of verbs and frames in English, since a systematic hierarchical clustering algorithm converges on the same structure as the handcrafted taxonomy of VerbNet, a broad-coverage verb lexicon. We also show that the hierarchies capture meaningful psychological dimensions of generalization by predicting novel verb coercions by human participants. We discuss limitations of a simple hierarchical representation and suggest similar approaches for identifying the representations underpinning verb argument structure.

null

10.18653/v1/D17-1104

null

57,592

inproceedings

calixto-liu-2017-incorporating

Incorporating Global Visual Features into Attention-based Neural Machine Translation.

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1105/

Calixto, Iacer and Liu, Qun

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

992--1003

We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. In our experiments, we evaluate translations into English and German, how different strategies to incorporate global image features compare and which ones perform best. We also study the impact that adding synthetic multi-modal, multilingual data brings and find that the additional data have a positive impact on multi-modal NMT models. We report new state-of-the-art results and our best models also significantly improve on a comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k data set according to all metrics evaluated. To the best of our knowledge, it is the first time a purely neural model significantly improves over a PBSMT model on all metrics evaluated on this data set.

null

10.18653/v1/D17-1105

null

57,593

inproceedings

misra-etal-2017-mapping

Mapping Instructions and Visual Observations to Actions with Reinforcement Learning

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1106/

Misra, Dipendra and Langford, John and Artzi, Yoav

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

1004--1015

We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent`s exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants.

null

10.18653/v1/D17-1106

null

57,594

inproceedings

fraser-etal-2017-analysis

An analysis of eye-movements during reading for the detection of mild cognitive impairment

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1107/

Fraser, Kathleen C. and Lundholm Fors, Kristina and Kokkinakis, Dimitrios and Nordlund, Arto

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

1016--1026

We present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia. We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging). Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features. Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86{\%} accuracy.

null

10.18653/v1/D17-1107

null

57,595

inproceedings

ning-etal-2017-structured

A Structured Learning Approach to Temporal Relation Extraction

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1108/

Ning, Qiang and Feng, Zhili and Roth, Dan

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

1027--1037

Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.

null

10.18653/v1/D17-1108

null

57,596

inproceedings

chaganty-etal-2017-importance

Importance sampling for unbiased on-demand evaluation of knowledge base population

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1109/

Chaganty, Arun and Paranjape, Ashwin and Liang, Percy and Manning, Christopher D.

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

1038--1048

Knowledge base population (KBP) systems take in a large document corpus and extract entities and their relations. Thus far, KBP evaluation has relied on judgements on the pooled predictions of existing systems. We show that this evaluation is problematic: when a new system predicts a previously unseen relation, it is penalized even if it is correct. This leads to significant bias against new systems, which counterproductively discourages innovation in the field. Our first contribution is a new importance-sampling based evaluation which corrects for this bias by annotating a new system`s predictions on-demand via crowdsourcing. We show this eliminates bias and reduces variance using data from the 2015 TAC KBP task. Our second contribution is an implementation of our method made publicly available as an online KBP evaluation service. We pilot the service by testing diverse state-of-the-art systems on the TAC KBP 2016 corpus and obtain accurate scores in a cost effective manner.

null

10.18653/v1/D17-1109

null

57,597

inproceedings

hui-etal-2017-pacrr

{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1110/

Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

1049--1058

In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks.

null

10.18653/v1/D17-1110

null

57,598

inproceedings

raiman-miller-2017-globally

Globally Normalized Reader

Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian

sep

2017

Copenhagen, Denmark

Association for Computational Linguistics

https://aclanthology.org/D17-1111/

Raiman, Jonathan and Miller, John

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

1059--1069

Rapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bi-directional attention mechanisms or score all possible answer spans, limiting scalability. We propose instead to cast extractive QA as an iterative search problem: select the answer`s sentence, start word, and end word. This representation reduces the space of each search step and allows computation to be conditionally allocated to promising search paths. We show that globally normalizing the decision process and back-propagating through beam search makes this representation viable and learning efficient. We empirically demonstrate the benefits of this approach using our model, Globally Normalized Reader (GNR), which achieves the second highest single model performance on the Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster than bi-attention-flow. We also introduce a data-augmentation method to produce semantically valid examples by aligning named entities to a knowledge base and swapping them with new entities of the same type. This method improves the performance of all models considered in this work and is of independent interest for a variety of NLP tasks.

null

10.18653/v1/D17-1111

null

57,599