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Self-Regulated Interactive Sequence-to-Sequence Learning | Not all types of supervision signals are created equal: Different types of
feedback have different costs and effects on learning. We show how
self-regulation strategies that decide when to ask for which kind of feedback
from a teacher (or from oneself) can be cast as a learning-to-learn problem
leading to improved cost-aware sequence-to-sequence learning. In experiments on
interactive neural machine translation, we find that the self-regulator
discovers an $\epsilon$-greedy strategy for the optimal cost-quality trade-off
by mixing different feedback types including corrections, error markups, and
self-supervision. Furthermore, we demonstrate its robustness under domain shift
and identify it as a promising alternative to active learning.
| 2,019 | Computation and Language |
Large Memory Layers with Product Keys | This paper introduces a structured memory which can be easily integrated into
a neural network. The memory is very large by design and significantly
increases the capacity of the architecture, by up to a billion parameters with
a negligible computational overhead. Its design and access pattern is based on
product keys, which enable fast and exact nearest neighbor search. The ability
to increase the number of parameters while keeping the same computational
budget lets the overall system strike a better trade-off between prediction
accuracy and computation efficiency both at training and test time. This memory
layer allows us to tackle very large scale language modeling tasks. In our
experiments we consider a dataset with up to 30 billion words, and we plug our
memory layer in a state-of-the-art transformer-based architecture. In
particular, we found that a memory augmented model with only 12 layers
outperforms a baseline transformer model with 24 layers, while being twice
faster at inference time. We release our code for reproducibility purposes.
| 2,019 | Computation and Language |
Improving Neural Relation Extraction with Implicit Mutual Relations | Relation extraction (RE) aims at extracting the relation between two entities
from the text corpora. It is a crucial task for Knowledge Graph (KG)
construction. Most existing methods predict the relation between an entity pair
by learning the relation from the training sentences, which contain the
targeted entity pair. In contrast to existing distant supervision approaches
that suffer from insufficient training corpora to extract relations, our
proposal of mining implicit mutual relation from the massive unlabeled corpora
transfers the semantic information of entity pairs into the RE model, which is
more expressive and semantically plausible. After constructing an entity
proximity graph based on the implicit mutual relations, we preserve the
semantic relations of entity pairs via embedding each vertex of the graph into
a low-dimensional space. As a result, we can easily and flexibly integrate the
implicit mutual relations and other entity information, such as entity types,
into the existing RE methods.
Our experimental results on a New York Times and another Google Distant
Supervision datasets suggest that our proposed neural RE framework provides a
promising improvement for the RE task, and significantly outperforms the
state-of-the-art methods. Moreover, the component for mining implicit mutual
relations is so flexible that can help to improve the performance of both
CNN-based and RNN-based RE models significant.
| 2,020 | Computation and Language |
Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a
Correntropy Objective Function | Translation-based embedding models have gained significant attention in link
prediction tasks for knowledge graphs. TransE is the primary model among
translation-based embeddings and is well-known for its low complexity and high
efficiency. Therefore, most of the earlier works have modified the score
function of the TransE approach in order to improve the performance of link
prediction tasks. Nevertheless, proven theoretically and experimentally, the
performance of TransE strongly depends on the loss function. Margin Ranking
Loss (MRL) has been one of the earlier loss functions which is widely used for
training TransE. However, the scores of positive triples are not necessarily
enforced to be sufficiently small to fulfill the translation from head to tail
by using relation vector (original assumption of TransE). To tackle this
problem, several loss functions have been proposed recently by adding upper
bounds and lower bounds to the scores of positive and negative samples.
Although highly effective, previously developed models suffer from an expansion
in search space for a selection of the hyperparameters (in particular the upper
and lower bounds of scores) on which the performance of the translation-based
models is highly dependent. In this paper, we propose a new loss function
dubbed Adaptive Margin Loss (AML) for training translation-based embedding
models. The formulation of the proposed loss function enables an adaptive and
automated adjustment of the margin during the learning process. Therefore,
instead of obtaining two values (upper bound and lower bound), only the center
of a margin needs to be determined. During learning, the margin is expanded
automatically until it converges. In our experiments on a set of standard
benchmark datasets including Freebase and WordNet, the effectiveness of AML is
confirmed for training TransE on link prediction tasks.
| 2,019 | Computation and Language |
Joint Speech Recognition and Speaker Diarization via Sequence
Transduction | Speech applications dealing with conversations require not only recognizing
the spoken words, but also determining who spoke when. The task of assigning
words to speakers is typically addressed by merging the outputs of two separate
systems, namely, an automatic speech recognition (ASR) system and a speaker
diarization (SD) system. The two systems are trained independently with
different objective functions. Often the SD systems operate directly on the
acoustics and are not constrained to respect word boundaries and this
deficiency is overcome in an ad hoc manner. Motivated by recent advances in
sequence to sequence learning, we propose a novel approach to tackle the two
tasks by a joint ASR and SD system using a recurrent neural network transducer.
Our approach utilizes both linguistic and acoustic cues to infer speaker roles,
as opposed to typical SD systems, which only use acoustic cues. We evaluated
the performance of our approach on a large corpus of medical conversations
between physicians and patients. Compared to a competitive conventional
baseline, our approach improves word-level diarization error rate from 15.8% to
2.2%.
| 2,019 | Computation and Language |
To Tune or Not To Tune? How About the Best of Both Worlds? | The introduction of pre-trained language models has revolutionized natural
language research communities. However, researchers still know relatively
little regarding their theoretical and empirical properties. In this regard,
Peters et al. perform several experiments which demonstrate that it is better
to adapt BERT with a light-weight task-specific head, rather than building a
complex one on top of the pre-trained language model, and freeze the parameters
in the said language model. However, there is another option to adopt. In this
paper, we propose a new adaptation method which we first train the task model
with the BERT parameters frozen and then fine-tune the entire model together.
Our experimental results show that our model adaptation method can achieve 4.7%
accuracy improvement in semantic similarity task, 0.99% accuracy improvement in
sequence labeling task and 0.72% accuracy improvement in the text
classification task.
| 2,019 | Computation and Language |
ReCoSa: Detecting the Relevant Contexts with Self-Attention for
Multi-turn Dialogue Generation | In multi-turn dialogue generation, response is usually related with only a
few contexts. Therefore, an ideal model should be able to detect these relevant
contexts and produce a suitable response accordingly. However, the widely used
hierarchical recurrent encoderdecoder models just treat all the contexts
indiscriminately, which may hurt the following response generation process.
Some researchers try to use the cosine similarity or the traditional attention
mechanism to find the relevant contexts, but they suffer from either
insufficient relevance assumption or position bias problem. In this paper, we
propose a new model, named ReCoSa, to tackle this problem. Firstly, a word
level LSTM encoder is conducted to obtain the initial representation of each
context. Then, the self-attention mechanism is utilized to update both the
context and masked response representation. Finally, the attention weights
between each context and response representations are computed and used in the
further decoding process. Experimental results on both Chinese customer
services dataset and English Ubuntu dialogue dataset show that ReCoSa
significantly outperforms baseline models, in terms of both metric-based and
human evaluations. Further analysis on attention shows that the detected
relevant contexts by ReCoSa are highly coherent with human's understanding,
validating the correctness and interpretability of ReCoSa.
| 2,019 | Computation and Language |
Neural or Statistical: An Empirical Study on Language Models for Chinese
Input Recommendation on Mobile | Chinese input recommendation plays an important role in alleviating human
cost in typing Chinese words, especially in the scenario of mobile
applications. The fundamental problem is to predict the conditional probability
of the next word given the sequence of previous words. Therefore, statistical
language models, i.e.~n-grams based models, have been extensively used on this
task in real application. However, the characteristics of extremely different
typing behaviors usually lead to serious sparsity problem, even n-gram with
smoothing will fail. A reasonable approach to tackle this problem is to use the
recently proposed neural models, such as probabilistic neural language model,
recurrent neural network and word2vec. They can leverage more semantically
similar words for estimating the probability. However, there is no conclusion
on which approach of the two will work better in real application. In this
paper, we conduct an extensive empirical study to show the differences between
statistical and neural language models. The experimental results show that the
two different approach have individual advantages, and a hybrid approach will
bring a significant improvement.
| 2,017 | Computation and Language |
Semantic Parsing with Dual Learning | Semantic parsing converts natural language queries into structured logical
forms. The paucity of annotated training samples is a fundamental challenge in
this field. In this work, we develop a semantic parsing framework with the dual
learning algorithm, which enables a semantic parser to make full use of data
(labeled and even unlabeled) through a dual-learning game. This game between a
primal model (semantic parsing) and a dual model (logical form to query) forces
them to regularize each other, and can achieve feedback signals from some
prior-knowledge. By utilizing the prior-knowledge of logical form structures,
we propose a novel reward signal at the surface and semantic levels which tends
to generate complete and reasonable logical forms. Experimental results show
that our approach achieves new state-of-the-art performance on ATIS dataset and
gets competitive performance on Overnight dataset.
| 2,019 | Computation and Language |
A Modular Task-oriented Dialogue System Using a Neural
Mixture-of-Experts | End-to-end Task-oriented Dialogue Systems (TDSs) have attracted a lot of
attention for their superiority (e.g., in terms of global optimization) over
pipeline modularized TDSs. Previous studies on end-to-end TDSs use a
single-module model to generate responses for complex dialogue contexts.
However, no model consistently outperforms the others in all cases. We propose
a neural Modular Task-oriented Dialogue System(MTDS) framework, in which a few
expert bots are combined to generate the response for a given dialogue context.
MTDS consists of a chair bot and several expert bots. Each expert bot is
specialized for a particular situation, e.g., one domain, one type of action of
a system, etc. The chair bot coordinates multiple expert bots and adaptively
selects an expert bot to generate the appropriate response. We further propose
a Token-level Mixture-of-Expert (TokenMoE) model to implement MTDS, where the
expert bots predict multiple tokens at each timestamp and the chair bot
determines the final generated token by fully taking into consideration the
outputs of all expert bots. Both the chair bot and the expert bots are jointly
trained in an end-to-end fashion. To verify the effectiveness of TokenMoE, we
carry out extensive experiments on a benchmark dataset. Compared with the
baseline using a single-module model, our TokenMoE improves the performance by
8.1% of inform rate and 0.8% of success rate.
| 2,019 | Computation and Language |
Incrementalizing RASA's Open-Source Natural Language Understanding
Pipeline | As spoken dialogue systems and chatbots are gaining more widespread adoption,
commercial and open-sourced services for natural language understanding are
emerging. In this paper, we explain how we altered the open-source RASA natural
language understanding pipeline to process incrementally (i.e., word-by-word),
following the incremental unit framework proposed by Schlangen and Skantze. To
do so, we altered existing RASA components to process incrementally, and added
an update-incremental intent recognition model as a component to RASA. Our
evaluations on the Snips dataset show that our changes allow RASA to function
as an effective incremental natural language understanding service.
| 2,019 | Computation and Language |
Solving Hard Coreference Problems | Coreference resolution is a key problem in natural language understanding
that still escapes reliable solutions. One fundamental difficulty has been that
of resolving instances involving pronouns since they often require deep
language understanding and use of background knowledge. In this paper, we
propose an algorithmic solution that involves a new representation for the
knowledge required to address hard coreference problems, along with a
constrained optimization framework that uses this knowledge in coreference
decision making. Our representation, Predicate Schemas, is instantiated with
knowledge acquired in an unsupervised way, and is compiled automatically into
constraints that impact the coreference decision. We present a general
coreference resolution system that significantly improves state-of-the-art
performance on hard, Winograd-style, pronoun resolution cases, while still
performing at the state-of-the-art level on standard coreference resolution
datasets.
| 2,019 | Computation and Language |
The Dynamic Embedded Topic Model | Topic modeling analyzes documents to learn meaningful patterns of words. For
documents collected in sequence, dynamic topic models capture how these
patterns vary over time. We develop the dynamic embedded topic model (D-ETM), a
generative model of documents that combines dynamic latent Dirichlet allocation
(D-LDA) and word embeddings. The D-ETM models each word with a categorical
distribution parameterized by the inner product between the word embedding and
a per-time-step embedding representation of its assigned topic. The D-ETM
learns smooth topic trajectories by defining a random walk prior over the
embedding representations of the topics. We fit the D-ETM using structured
amortized variational inference with a recurrent neural network. On three
different corpora---a collection of United Nations debates, a set of ACL
abstracts, and a dataset of Science Magazine articles---we found that the D-ETM
outperforms D-LDA on a document completion task. We further found that the
D-ETM learns more diverse and coherent topics than D-LDA while requiring
significantly less time to fit.
| 2,019 | Computation and Language |
Neural News Recommendation with Attentive Multi-View Learning | Personalized news recommendation is very important for online news platforms
to help users find interested news and improve user experience. News and user
representation learning is critical for news recommendation. Existing news
recommendation methods usually learn these representations based on single news
information, e.g., title, which may be insufficient. In this paper we propose a
neural news recommendation approach which can learn informative representations
of users and news by exploiting different kinds of news information. The core
of our approach is a news encoder and a user encoder. In the news encoder we
propose an attentive multi-view learning model to learn unified news
representations from titles, bodies and topic categories by regarding them as
different views of news. In addition, we apply both word-level and view-level
attention mechanism to news encoder to select important words and views for
learning informative news representations. In the user encoder we learn the
representations of users based on their browsed news and apply attention
mechanism to select informative news for user representation learning.
Extensive experiments on a real-world dataset show our approach can effectively
improve the performance of news recommendation.
| 2,019 | Computation and Language |
GRN: Gated Relation Network to Enhance Convolutional Neural Network for
Named Entity Recognition | The dominant approaches for named entity recognition (NER) mostly adopt
complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM).
However, RNNs are limited by their recurrent nature in terms of computational
efficiency. In contrast, convolutional neural networks (CNN) can fully exploit
the GPU parallelism with their feedforward architectures. However, little
attention has been paid to performing NER with CNNs, mainly owing to their
difficulties in capturing the long-term context information in a sequence. In
this paper, we propose a simple but effective CNN-based network for NER, i.e.,
gated relation network (GRN), which is more capable than common CNNs in
capturing long-term context. Specifically, in GRN we firstly employ CNNs to
explore the local context features of each word. Then we model the relations
between words and use them as gates to fuse local context features into global
ones for predicting labels. Without using recurrent layers that process a
sentence in a sequential manner, our GRN allows computations to be performed in
parallel across the entire sentence. Experiments on two benchmark NER datasets
(i.e., CoNLL2003 and Ontonotes 5.0) show that, our proposed GRN can achieve
state-of-the-art performance with or without external knowledge. It also enjoys
lower time costs to train and test.We have made the code publicly available at
https://github.com/HuiChen24/NER-GRN.
| 2,019 | Computation and Language |
Automated Word Stress Detection in Russian | In this study we address the problem of automated word stress detection in
Russian using character level models and no part-speech-taggers. We use a
simple bidirectional RNN with LSTM nodes and achieve the accuracy of 90% or
higher. We experiment with two training datasets and show that using the data
from an annotated corpus is much more efficient than using a dictionary, since
it allows us to take into account word frequencies and the morphological
context of the word.
| 2,017 | Computation and Language |
Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained
Language Models for Task-Oriented Dialogue Systems | Data scarcity is a long-standing and crucial challenge that hinders quick
development of task-oriented dialogue systems across multiple domains:
task-oriented dialogue models are expected to learn grammar, syntax, dialogue
reasoning, decision making, and language generation from absurdly small amounts
of task-specific data. In this paper, we demonstrate that recent progress in
language modeling pre-training and transfer learning shows promise to overcome
this problem. We propose a task-oriented dialogue model that operates solely on
text input: it effectively bypasses explicit policy and language generation
modules. Building on top of the TransferTransfo framework (Wolf et al., 2019)
and generative model pre-training (Radford et al., 2019), we validate the
approach on complex multi-domain task-oriented dialogues from the MultiWOZ
dataset. Our automatic and human evaluations show that the proposed model is on
par with a strong task-specific neural baseline. In the long run, our approach
holds promise to mitigate the data scarcity problem, and to support the
construction of more engaging and more eloquent task-oriented conversational
agents.
| 2,019 | Computation and Language |
Generating Sentences from Disentangled Syntactic and Semantic Spaces | Variational auto-encoders (VAEs) are widely used in natural language
generation due to the regularization of the latent space. However, generating
sentences from the continuous latent space does not explicitly model the
syntactic information. In this paper, we propose to generate sentences from
disentangled syntactic and semantic spaces. Our proposed method explicitly
models syntactic information in the VAE's latent space by using the linearized
tree sequence, leading to better performance of language generation.
Additionally, the advantage of sampling in the disentangled syntactic and
semantic latent spaces enables us to perform novel applications, such as the
unsupervised paraphrase generation and syntax-transfer generation. Experimental
results show that our proposed model achieves similar or better performance in
various tasks, compared with state-of-the-art related work.
| 2,019 | Computation and Language |
Qwant Research @DEFT 2019: Document matching and information retrieval
using clinical cases | This paper reports on Qwant Research contribution to tasks 2 and 3 of the
DEFT 2019's challenge, focusing on French clinical cases analysis. Task 2 is a
task on semantic similarity between clinical cases and discussions. For this
task, we propose an approach based on language models and evaluate the impact
on the results of different preprocessings and matching techniques. For task 3,
we have developed an information extraction system yielding very encouraging
results accuracy-wise. We have experimented two different approaches, one based
on the exclusive use of neural networks, the other based on a linguistic
analysis.
| 2,019 | Computation and Language |
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from
Wikipedia | We present an approach based on multilingual sentence embeddings to
automatically extract parallel sentences from the content of Wikipedia articles
in 85 languages, including several dialects or low-resource languages. We do
not limit the the extraction process to alignments with English, but
systematically consider all possible language pairs. In total, we are able to
extract 135M parallel sentences for 1620 different language pairs, out of which
only 34M are aligned with English. This corpus of parallel sentences is freely
available at
https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix. To get
an indication on the quality of the extracted bitexts, we train neural MT
baseline systems on the mined data only for 1886 languages pairs, and evaluate
them on the TED corpus, achieving strong BLEU scores for many language pairs.
The WikiMatrix bitexts seem to be particularly interesting to train MT systems
between distant languages without the need to pivot through English.
| 2,019 | Computation and Language |
Knowledge-incorporating ESIM models for Response Selection in
Retrieval-based Dialog Systems | Goal-oriented dialog systems, which can be trained end-to-end without
manually encoding domain-specific features, show tremendous promise in the
customer support use-case e.g. flight booking, hotel reservation, technical
support, student advising etc. These dialog systems must learn to interact with
external domain knowledge to achieve the desired goal e.g. recommending courses
to a student, booking a table at a restaurant etc. This paper presents extended
Enhanced Sequential Inference Model (ESIM) models: a) K-ESIM (Knowledge-ESIM),
which incorporates the external domain knowledge and b) T-ESIM (Targeted-ESIM),
which leverages information from similar conversations to improve the
prediction accuracy. Our proposed models and the baseline ESIM model are
evaluated on the Ubuntu and Advising datasets in the Sentence Selection track
of the latest Dialog System Technology Challenge (DSTC7), where the goal is to
find the correct next utterance, given a partial conversation, from a set of
candidates. Our preliminary results suggest that incorporating external
knowledge sources and leveraging information from similar dialogs leads to
performance improvements for predicting the next utterance.
| 2,019 | Computation and Language |
Equiprobable mappings in weighted constraint grammars | We show that MaxEnt is so rich that it can distinguish between any two
different mappings: there always exists a nonnegative weight vector which
assigns them different MaxEnt probabilities. Stochastic HG instead does admit
equiprobable mappings and we give a complete formal characterization of them.
We compare these different predictions of the two frameworks on a test case of
Finnish stress.
| 2,019 | Computation and Language |
The University of Edinburgh's Submissions to the WMT19 News Translation
Task | The University of Edinburgh participated in the WMT19 Shared Task on News
Translation in six language directions: English-to-Gujarati,
Gujarati-to-English, English-to-Chinese, Chinese-to-English, German-to-English,
and English-to-Czech. For all translation directions, we created or used
back-translations of monolingual data in the target language as additional
synthetic training data. For English-Gujarati, we also explored semi-supervised
MT with cross-lingual language model pre-training, and translation pivoting
through Hindi. For translation to and from Chinese, we investigated
character-based tokenisation vs. sub-word segmentation of Chinese text. For
German-to-English, we studied the impact of vast amounts of back-translated
training data on translation quality, gaining a few additional insights over
Edunov et al. (2018). For English-to-Czech, we compared different
pre-processing and tokenisation regimes.
| 2,019 | Computation and Language |
PyKaldi2: Yet another speech toolkit based on Kaldi and PyTorch | We introduce PyKaldi2 speech recognition toolkit implemented based on Kaldi
and PyTorch. While similar toolkits are available built on top of the two, a
key feature of PyKaldi2 is sequence training with criteria such as MMI, sMBR
and MPE. In particular, we implemented the sequence training module with
on-the-fly lattice generation during model training in order to simplify the
training pipeline. To address the challenging acoustic environments in real
applications, PyKaldi2 also supports on-the-fly noise and reverberation
simulation to improve the model robustness. With this feature, it is possible
to backpropogate the gradients from the sequence-level loss to the front-end
feature extraction module, which, hopefully, can foster more research in the
direction of joint front-end and backend learning. We performed benchmark
experiments on Librispeech, and show that PyKaldi2 can achieve reasonable
recognition accuracy. The toolkit is released under the MIT license.
| 2,019 | Computation and Language |
Cross-Lingual Transfer Learning for Question Answering | Deep learning based question answering (QA) on English documents has achieved
success because there is a large amount of English training examples. However,
for most languages, training examples for high-quality QA models are not
available. In this paper, we explore the problem of cross-lingual transfer
learning for QA, where a source language task with plentiful annotations is
utilized to improve the performance of a QA model on a target language task
with limited available annotations. We examine two different approaches. A
machine translation (MT) based approach translates the source language into the
target language, or vice versa. Although the MT-based approach brings
improvement, it assumes the availability of a sentence-level translation
system. A GAN-based approach incorporates a language discriminator to learn
language-universal feature representations, and consequentially transfer
knowledge from the source language. The GAN-based approach rivals the
performance of the MT-based approach with fewer linguistic resources. Applying
both approaches simultaneously yield the best results. We use two English
benchmark datasets, SQuAD and NewsQA, as source language data, and show
significant improvements over a number of established baselines on a Chinese QA
task. We achieve the new state-of-the-art on the Chinese QA dataset.
| 2,019 | Computation and Language |
A Relational Memory-based Embedding Model for Triple Classification and
Search Personalization | Knowledge graph embedding methods often suffer from a limitation of
memorizing valid triples to predict new ones for triple classification and
search personalization problems. To this end, we introduce a novel embedding
model, named R-MeN, that explores a relational memory network to encode
potential dependencies in relationship triples. R-MeN considers each triple as
a sequence of 3 input vectors that recurrently interact with a memory using a
transformer self-attention mechanism. Thus R-MeN encodes new information from
interactions between the memory and each input vector to return a corresponding
vector. Consequently, R-MeN feeds these 3 returned vectors to a convolutional
neural network-based decoder to produce a scalar score for the triple.
Experimental results show that our proposed R-MeN obtains state-of-the-art
results on SEARCH17 for the search personalization task, and on WN11 and FB13
for the triple classification task.
| 2,020 | Computation and Language |
Tackling Graphical NLP problems with Graph Recurrent Networks | How to properly model graphs is a long-existing and important problem in NLP
area, where several popular types of graphs are knowledge graphs, semantic
graphs and dependency graphs. Comparing with other data structures, such as
sequences and trees, graphs are generally more powerful in representing complex
correlations among entities. For example, a knowledge graph stores real-word
entities (such as "Barack_Obama" and "U.S.") and their relations (such as
"live_in" and "lead_by"). Properly encoding a knowledge graph is beneficial to
user applications, such as question answering and knowledge discovery. Modeling
graphs is also very challenging, probably because graphs usually contain
massive and cyclic relations.
Recent years have witnessed the success of deep learning, especially
RNN-based models, on many NLP problems. Besides, RNNs and their variations have
been extensively studied on several graph problems and showed preliminary
successes. Despite the successes that have been achieved, RNN-based models
suffer from several major drawbacks on graphs. First, they can only consume
sequential data, thus linearization is required to serialize input graphs,
resulting in the loss of important structural information. Second, the
serialization results are usually very long, so it takes a long time for RNNs
to encode them.
In this thesis, we propose a novel graph neural network, named graph
recurrent network (GRN). We study our GRN model on 4 very different tasks, such
as machine reading comprehension, relation extraction and machine translation.
Some take undirected graphs without edge labels, while the others have directed
ones with edge labels. To consider these important differences, we gradually
enhance our GRN model, such as further considering edge labels and adding an
RNN decoder. Carefully designed experiments show the effectiveness of GRN on
all these tasks.
| 2,019 | Computation and Language |
Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level
Neural Machine Translation | This paper describes the Microsoft Translator submissions to the WMT19 news
translation shared task for English-German. Our main focus is document-level
neural machine translation with deep transformer models. We start with strong
sentence-level baselines, trained on large-scale data created via
data-filtering and noisy back-translation and find that back-translation seems
to mainly help with translationese input. We explore fine-tuning techniques,
deeper models and different ensembling strategies to counter these effects.
Using document boundaries present in the authentic and synthetic parallel data,
we create sequences of up to 1000 subword segments and train transformer
translation models. We experiment with data augmentation techniques for the
smaller authentic data with document-boundaries and for larger authentic data
without boundaries. We further explore multi-task training for the
incorporation of document-level source language monolingual data via the
BERT-objective on the encoder and two-pass decoding for combinations of
sentence-level and document-level systems. Based on preliminary human
evaluation results, evaluators strongly prefer the document-level systems over
our comparable sentence-level system. The document-level systems also seem to
score higher than the human references in source-based direct assessment.
| 2,019 | Computation and Language |
Simple Automatic Post-editing for Arabic-Japanese Machine Translation | A common bottleneck for developing machine translation (MT) systems for some
language pairs is the lack of direct parallel translation data sets, in general
and in certain domains. Alternative solutions such as zero-shot models or
pivoting techniques are successful in getting a strong baseline, but are often
below the more supported language-pair systems. In this paper, we focus on
Arabic-Japanese machine translation, a less studied language pair; and we work
with a unique parallel corpus of Arabic news articles that were manually
translated to Japanese. We use this parallel corpus to adapt a state-of-the-art
domain/genre agnostic neural MT system via a simple automatic post-editing
technique. Our results and detailed analysis suggest that this approach is
quite viable for less supported language pairs in specific domains.
| 2,019 | Computation and Language |
Lexical Simplification with Pretrained Encoders | Lexical simplification (LS) aims to replace complex words in a given sentence
with their simpler alternatives of equivalent meaning. Recently unsupervised
lexical simplification approaches only rely on the complex word itself
regardless of the given sentence to generate candidate substitutions, which
will inevitably produce a large number of spurious candidates. We present a
simple LS approach that makes use of the Bidirectional Encoder Representations
from Transformers (BERT) which can consider both the given sentence and the
complex word during generating candidate substitutions for the complex word.
Specifically, we mask the complex word of the original sentence for feeding
into the BERT to predict the masked token. The predicted results will be used
as candidate substitutions. Despite being entirely unsupervised, experimental
results show that our approach obtains obvious improvement compared with these
baselines leveraging linguistic databases and parallel corpus, outperforming
the state-of-the-art by more than 12 Accuracy points on three well-known
benchmarks.
| 2,020 | Computation and Language |
TWEETQA: A Social Media Focused Question Answering Dataset | With social media becoming increasingly pop-ular on which lots of news and
real-time eventsare reported, developing automated questionanswering systems is
critical to the effective-ness of many applications that rely on real-time
knowledge. While previous datasets haveconcentrated on question answering (QA)
forformal text like news and Wikipedia, wepresent the first large-scale dataset
for QA oversocial media data. To ensure that the tweetswe collected are useful,
we only gather tweetsused by journalists to write news articles. Wethen ask
human annotators to write questionsand answers upon these tweets. Unlike
otherQA datasets like SQuAD in which the answersare extractive, we allow the
answers to be ab-stractive. We show that two recently proposedneural models
that perform well on formaltexts are limited in their performance when ap-plied
to our dataset. In addition, even the fine-tuned BERT model is still lagging
behind hu-man performance with a large margin. Our re-sults thus point to the
need of improved QAsystems targeting social media text.
| 2,019 | Computation and Language |
Joint Language Identification of Code-Switching Speech using Attention
based E2E Network | Language identification (LID) has relevance in many speech processing
applications. For the automatic recognition of code-switching speech, the
conventional approaches often employ an LID system for detecting the languages
present within an utterance. In the existing works, the LID on code-switching
speech involves modelling of the underlying languages separately. In this work,
we propose a joint modelling based LID system for code-switching speech. To
achieve the same, an attention-based end-to-end (E2E) network has been
explored. For the development and evaluation of the proposed approach, a
recently created Hindi-English code-switching corpus has been used. For the
contrast purpose, an LID system employing the connectionist temporal
classification-based E2E network is also developed. On comparing both the LID
systems, the attention based approach is noted to result in better LID
accuracy. The effective location of code-switching boundaries within the
utterance by the proposed approach has been demonstrated by plotting the
attention weights of E2E network.
| 2,019 | Computation and Language |
GLOSS: Generative Latent Optimization of Sentence Representations | We propose a method to learn unsupervised sentence representations in a
non-compositional manner based on Generative Latent Optimization. Our approach
does not impose any assumptions on how words are to be combined into a sentence
representation. We discuss a simple Bag of Words model as well as a variant
that models word positions. Both are trained to reconstruct the sentence based
on a latent code and our model can be used to generate text. Experiments show
large improvements over the related Paragraph Vectors. Compared to uSIF, we
achieve a relative improvement of 5% when trained on the same data and our
method performs competitively to Sent2vec while trained on 30 times less data.
| 2,019 | Computation and Language |
Investigation on N-gram Approximated RNNLMs for Recognition of
Morphologically Rich Speech | Recognition of Hungarian conversational telephone speech is challenging due
to the informal style and morphological richness of the language. Recurrent
Neural Network Language Model (RNNLM) can provide remedy for the high
perplexity of the task; however, two-pass decoding introduces a considerable
processing delay. In order to eliminate this delay we investigate approaches
aiming at the complexity reduction of RNNLM, while preserving its accuracy. We
compare the performance of conventional back-off n-gram language models (BNLM),
BNLM approximation of RNNLMs (RNN-BNLM) and RNN n-grams in terms of perplexity
and word error rate (WER). Morphological richness is often addressed by using
statistically derived subwords - morphs - in the language models, hence our
investigations are extended to morph-based models, as well. We found that using
RNN-BNLMs 40% of the RNNLM perplexity reduction can be recovered, which is
roughly equal to the performance of a RNN 4-gram model. Combining morph-based
modeling and approximation of RNNLM, we were able to achieve 8% relative WER
reduction and preserve real-time operation of our conversational telephone
speech recognition system.
| 2,020 | Computation and Language |
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta
vertex aggregation | Keyword extraction is used for summarizing the content of a document and
supports efficient document retrieval, and is as such an indispensable part of
modern text-based systems. We explore how load centrality, a graph-theoretic
measure applied to graphs derived from a given text can be used to efficiently
identify and rank keywords. Introducing meta vertices (aggregates of existing
vertices) and systematic redundancy filters, the proposed method performs on
par with state-of-the-art for the keyword extraction task on 14 diverse
datasets. The proposed method is unsupervised, interpretable and can also be
used for document visualization.
| 2,019 | Computation and Language |
Naver Labs Europe's Systems for the WMT19 Machine Translation Robustness
Task | This paper describes the systems that we submitted to the WMT19 Machine
Translation robustness task. This task aims to improve MT's robustness to noise
found on social media, like informal language, spelling mistakes and other
orthographic variations. The organizers provide parallel data extracted from a
social media website in two language pairs: French-English and Japanese-English
(in both translation directions). The goal is to obtain the best scores on
unseen test sets from the same source, according to automatic metrics (BLEU)
and human evaluation. We proposed one single and one ensemble system for each
translation direction. Our ensemble models ranked first in all language pairs,
according to BLEU evaluation. We discuss the pre-processing choices that we
made, and present our solutions for robustness to noise and domain adaptation.
| 2,019 | Computation and Language |
Asking Clarifying Questions in Open-Domain Information-Seeking
Conversations | Users often fail to formulate their complex information needs in a single
query. As a consequence, they may need to scan multiple result pages or
reformulate their queries, which may be a frustrating experience.
Alternatively, systems can improve user satisfaction by proactively asking
questions of the users to clarify their information needs. Asking clarifying
questions is especially important in conversational systems since they can only
return a limited number of (often only one) result(s). In this paper, we
formulate the task of asking clarifying questions in open-domain
information-seeking conversational systems. To this end, we propose an offline
evaluation methodology for the task and collect a dataset, called Qulac,
through crowdsourcing. Our dataset is built on top of the TREC Web Track
2009-2012 data and consists of over 10K question-answer pairs for 198 TREC
topics with 762 facets. Our experiments on an oracle model demonstrate that
asking only one good question leads to over 170% retrieval performance
improvement in terms of P@1, which clearly demonstrates the potential impact of
the task. We further propose a retrieval framework consisting of three
components: question retrieval, question selection, and document retrieval. In
particular, our question selection model takes into account the original query
and previous question-answer interactions while selecting the next question.
Our model significantly outperforms competitive baselines. To foster research
in this area, we have made Qulac publicly available.
| 2,019 | Computation and Language |
Facebook FAIR's WMT19 News Translation Task Submission | This paper describes Facebook FAIR's submission to the WMT19 shared news
translation task. We participate in two language pairs and four language
directions, English <-> German and English <-> Russian. Following our
submission from last year, our baseline systems are large BPE-based transformer
models trained with the Fairseq sequence modeling toolkit which rely on sampled
back-translations. This year we experiment with different bitext data filtering
schemes, as well as with adding filtered back-translated data. We also ensemble
and fine-tune our models on domain-specific data, then decode using noisy
channel model reranking. Our submissions are ranked first in all four
directions of the human evaluation campaign. On En->De, our system
significantly outperforms other systems as well as human translations. This
system improves upon our WMT'18 submission by 4.5 BLEU points.
| 2,019 | Computation and Language |
Towards Near-imperceptible Steganographic Text | We show that the imperceptibility of several existing linguistic
steganographic systems (Fang et al., 2017; Yang et al., 2018) relies on
implicit assumptions on statistical behaviors of fluent text. We formally
analyze them and empirically evaluate these assumptions. Furthermore, based on
these observations, we propose an encoding algorithm called patient-Huffman
with improved near-imperceptible guarantees.
| 2,019 | Computation and Language |
Low-supervision urgency detection and transfer in short crisis messages | Humanitarian disasters have been on the rise in recent years due to the
effects of climate change and socio-political situations such as the refugee
crisis. Technology can be used to best mobilize resources such as food and
water in the event of a natural disaster, by semi-automatically flagging tweets
and short messages as indicating an urgent need. The problem is challenging not
just because of the sparseness of data in the immediate aftermath of a
disaster, but because of the varying characteristics of disasters in developing
countries (making it difficult to train just one system) and the noise and
quirks in social media. In this paper, we present a robust, low-supervision
social media urgency system that adapts to arbitrary crises by leveraging both
labeled and unlabeled data in an ensemble setting. The system is also able to
adapt to new crises where an unlabeled background corpus may not be available
yet by utilizing a simple and effective transfer learning methodology.
Experimentally, our transfer learning and low-supervision approaches are found
to outperform viable baselines with high significance on myriad disaster
datasets.
| 2,019 | Computation and Language |
A generic rule-based system for clinical trial patient selection | The n2c2 2018 Challenge task 1 aimed to identify patients who meet lists of
heterogeneous inclusion/exclusion criteria for a hypothetical clinical trial.
We demonstrate a generic rule-based natural language pipeline can support this
task with decent performance (the average F1 score on the test set is 0.89,
ranked the 8th out of 45 teams ).
| 2,019 | Computation and Language |
Language comparison via network topology | Modeling relations between languages can offer understanding of language
characteristics and uncover similarities and differences between languages.
Automated methods applied to large textual corpora can be seen as opportunities
for novel statistical studies of language development over time, as well as for
improving cross-lingual natural language processing techniques. In this work,
we first propose how to represent textual data as a directed, weighted network
by the text2net algorithm. We next explore how various fast,
network-topological metrics, such as network community structure, can be used
for cross-lingual comparisons. In our experiments, we employ eight different
network topology metrics, and empirically showcase on a parallel corpus, how
the methods can be used for modeling the relations between nine selected
languages. We demonstrate that the proposed method scales to large corpora
consisting of hundreds of thousands of aligned sentences on an of-the-shelf
laptop. We observe that on the one hand properties such as communities, capture
some of the known differences between the languages, while others can be seen
as novel opportunities for linguistic studies.
| 2,019 | Computation and Language |
Neural Language Model Based Training Data Augmentation for Weakly
Supervised Early Rumor Detection | The scarcity and class imbalance of training data are known issues in current
rumor detection tasks. We propose a straight-forward and general-purpose data
augmentation technique which is beneficial to early rumor detection relying on
event propagation patterns. The key idea is to exploit massive unlabeled event
data sets on social media to augment limited labeled rumor source tweets. This
work is based on rumor spreading patterns revealed by recent rumor studies and
semantic relatedness between labeled and unlabeled data. A state-of-the-art
neural language model (NLM) and large credibility-focused Twitter corpora are
employed to learn context-sensitive representations of rumor tweets. Six
different real-world events based on three publicly available rumor datasets
are employed in our experiments to provide a comparative evaluation of the
effectiveness of the method. The results show that our method can expand the
size of an existing rumor data set nearly by 200% and corresponding social
context (i.e., conversational threads) by 100% with reasonable quality.
Preliminary experiments with a state-of-the-art deep learning-based rumor
detection model show that augmented data can alleviate over-fitting and class
imbalance caused by limited train data and can help to train complex neural
networks (NNs). With augmented data, the performance of rumor detection can be
improved by 12.1% in terms of F-score. Our experiments also indicate that
augmented training data can help to generalize rumor detection models on unseen
rumors.
| 2,019 | Computation and Language |
RadioTalk: a large-scale corpus of talk radio transcripts | We introduce RadioTalk, a corpus of speech recognition transcripts sampled
from talk radio broadcasts in the United States between October of 2018 and
March of 2019. The corpus is intended for use by researchers in the fields of
natural language processing, conversational analysis, and the social sciences.
The corpus encompasses approximately 2.8 billion words of automatically
transcribed speech from 284,000 hours of radio, together with metadata about
the speech, such as geographical location, speaker turn boundaries, gender, and
radio program information. In this paper we summarize why and how we prepared
the corpus, give some descriptive statistics on stations, shows and speakers,
and carry out a few high-level analyses.
| 2,019 | Computation and Language |
You Write Like You Eat: Stylistic variation as a predictor of social
stratification | Inspired by Labov's seminal work on stylistic variation as a function of
social stratification, we develop and compare neural models that predict a
person's presumed socio-economic status, obtained through distant
supervision,from their writing style on social media. The focus of our work is
on identifying the most important stylistic parameters to predict
socio-economic group. In particular, we show the effectiveness of
morpho-syntactic features as stylistic predictors of socio-economic group,in
contrast to lexical features, which are good predictors of topic.
| 2,019 | Computation and Language |
STRASS: A Light and Effective Method for Extractive Summarization Based
on Sentence Embeddings | This paper introduces STRASS: Summarization by TRAnsformation Selection and
Scoring. It is an extractive text summarization method which leverages the
semantic information in existing sentence embedding spaces. Our method creates
an extractive summary by selecting the sentences with the closest embeddings to
the document embedding. The model learns a transformation of the document
embedding to minimize the similarity between the extractive summary and the
ground truth summary. As the transformation is only composed of a dense layer,
the training can be done on CPU, therefore, inexpensive. Moreover, inference
time is short and linear according to the number of sentences. As a second
contribution, we introduce the French CASS dataset, composed of judgments from
the French Court of cassation and their corresponding summaries. On this
dataset, our results show that our method performs similarly to the state of
the art extractive methods with effective training and inferring time.
| 2,019 | Computation and Language |
Learning Representation Mapping for Relation Detection in Knowledge Base
Question Answering | Relation detection is a core step in many natural language process
applications including knowledge base question answering. Previous efforts show
that single-fact questions could be answered with high accuracy. However, one
critical problem is that current approaches only get high accuracy for
questions whose relations have been seen in the training data. But for unseen
relations, the performance will drop rapidly. The main reason for this problem
is that the representations for unseen relations are missing. In this paper, we
propose a simple mapping method, named representation adapter, to learn the
representation mapping for both seen and unseen relations based on previously
learned relation embedding. We employ the adversarial objective and the
reconstruction objective to improve the mapping performance. We re-organize the
popular SimpleQuestion dataset to reveal and evaluate the problem of detecting
unseen relations. Experiments show that our method can greatly improve the
performance of unseen relations while the performance for those seen part is
kept comparable to the state-of-the-art. Our code and data are available at
https://github.com/wudapeng268/KBQA-Adapter.
| 2,019 | Computation and Language |
Fake News Detection as Natural Language Inference | This report describes the entry by the Intelligent Knowledge Management (IKM)
Lab in the WSDM 2019 Fake News Classification challenge. We treat the task as
natural language inference (NLI). We individually train a number of the
strongest NLI models as well as BERT. We ensemble these results and retrain
with noisy labels in two stages. We analyze transitivity relations in the train
and test sets and determine a set of test cases that can be reliably classified
on this basis. The remainder of test cases are classified by our ensemble. Our
entry achieves test set accuracy of 88.063% for 3rd place in the competition.
| 2,019 | Computation and Language |
Probing Neural Network Comprehension of Natural Language Arguments | We are surprised to find that BERT's peak performance of 77% on the Argument
Reasoning Comprehension Task reaches just three points below the average
untrained human baseline. However, we show that this result is entirely
accounted for by exploitation of spurious statistical cues in the dataset. We
analyze the nature of these cues and demonstrate that a range of models all
exploit them. This analysis informs the construction of an adversarial dataset
on which all models achieve random accuracy. Our adversarial dataset provides a
more robust assessment of argument comprehension and should be adopted as the
standard in future work.
| 2,019 | Computation and Language |
SUMBT: Slot-Utterance Matching for Universal and Scalable Belief
Tracking | In goal-oriented dialog systems, belief trackers estimate the probability
distribution of slot-values at every dialog turn. Previous neural approaches
have modeled domain- and slot-dependent belief trackers, and have difficulty in
adding new slot-values, resulting in lack of flexibility of domain ontology
configurations. In this paper, we propose a new approach to universal and
scalable belief tracker, called slot-utterance matching belief tracker (SUMBT).
The model learns the relations between domain-slot-types and slot-values
appearing in utterances through attention mechanisms based on contextual
semantic vectors. Furthermore, the model predicts slot-value labels in a
non-parametric way. From our experiments on two dialog corpora, WOZ 2.0 and
MultiWOZ, the proposed model showed performance improvement in comparison with
slot-dependent methods and achieved the state-of-the-art joint accuracy.
| 2,019 | Computation and Language |
Differentiable Disentanglement Filter: an Application Agnostic Core
Concept Discovery Probe | It has long been speculated that deep neural networks function by discovering
a hierarchical set of domain-specific core concepts or patterns, which are
further combined to recognize even more elaborate concepts for the
classification or other machine learning tasks. Meanwhile disentangling the
actual core concepts engrained in the word embeddings (like word2vec or BERT)
or deep convolutional image recognition neural networks (like PG-GAN) is
difficult and some success there has been achieved only recently. In this paper
we propose a novel neural network nonlinearity named Differentiable
Disentanglement Filter (DDF) which can be transparently inserted into any
existing neural network layer to automatically disentangle the core concepts
used by that layer. The DDF probe is inspired by the obscure properties of the
hyper-dimensional computing theory. The DDF proof-of-concept implementation is
shown to disentangle concepts within the neural 3D scene representation - a
task vital for visual grounding of natural language narratives.
| 2,019 | Computation and Language |
Almawave-SLU: A new dataset for SLU in Italian | The widespread use of conversational and question answering systems made it
necessary to improve the performances of speaker intent detection and
understanding of related semantic slots, i.e., Spoken Language Understanding
(SLU). Often, these tasks are approached with supervised learning methods,
which needs considerable labeled datasets. This paper presents the first
Italian dataset for SLU. It is derived through a semi-automatic procedure and
is used as a benchmark of various open source and commercial systems.
| 2,019 | Computation and Language |
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success
and Minimal Human Agent Use | Neural end-to-end goal-oriented dialog systems showed promise to reduce the
workload of human agents for customer service, as well as reduce wait time for
users. However, their inability to handle new user behavior at deployment has
limited their usage in real world. In this work, we propose an end-to-end
trainable method for neural goal-oriented dialog systems which handles new user
behaviors at deployment by transferring the dialog to a human agent
intelligently. The proposed method has three goals: 1) maximize user's task
success by transferring to human agents, 2) minimize the load on the human
agents by transferring to them only when it is essential and 3) learn online
from the human agent's responses to reduce human agents load further. We
evaluate our proposed method on a modified-bAbI dialog task that simulates the
scenario of new user behaviors occurring at test time. Experimental results
show that our proposed method is effective in achieving the desired goals.
| 2,019 | Computation and Language |
Gated Recurrent Neural Network Approach for Multilabel Emotion Detection
in Microblogs | People express their opinions and emotions freely in social media posts and
online reviews that contain valuable feedback for multiple stakeholders such as
businesses and political campaigns. Manually extracting opinions and emotions
from large volumes of such posts is an impossible task. Therefore, automated
processing of these posts to extract opinions and emotions is an important
research problem. However, human emotion detection is a challenging task due to
the complexity and nuanced nature. To overcome these barriers, researchers have
extensively used techniques such as deep learning, distant supervision, and
transfer learning. In this paper, we propose a novel Pyramid Attention Network
(PAN) based model for emotion detection in microblogs. The main advantage of
our approach is that PAN has the capability to evaluate sentences in different
perspectives to capture multiple emotions existing in a single text. The
proposed model was evaluated on a recently released dataset and the results
achieved the state-of-the-art accuracy of 58.9%.
| 2,019 | Computation and Language |
Analysis of Word Embeddings Using Fuzzy Clustering | In data dominated systems and applications, a concept of representing words
in a numerical format has gained a lot of attention. There are a few approaches
used to generate such a representation. An interesting issue that should be
considered is the ability of such representations - called embeddings - to
imitate human-based semantic similarity between words. In this study, we
perform a fuzzy-based analysis of vector representations of words, i.e., word
embeddings. We use two popular fuzzy clustering algorithms on count-based word
embeddings, known as GloVe, of different dimensionality. Words from
WordSim-353, called the gold standard, are represented as vectors and
clustered. The results indicate that fuzzy clustering algorithms are very
sensitive to high-dimensional data, and parameter tuning can dramatically
change their performance. We show that by adjusting the value of the fuzzifier
parameter, fuzzy clustering can be successfully applied to vectors of high - up
to one hundred - dimensions. Additionally, we illustrate that fuzzy clustering
allows to provide interesting results regarding membership of words to
different clusters.
| 2,020 | Computation and Language |
Comparison of Classical Machine Learning Approaches on Bangla Textual
Emotion Analysis | Detecting emotions from text is an extension of simple sentiment polarity
detection. Instead of considering only positive or negative sentiments,
emotions are conveyed using more tangible manner; thus, they can be expressed
as many shades of gray. This paper manifests the results of our experimentation
for fine-grained emotion analysis on Bangla text. We gathered and annotated a
text corpus consisting of user comments from several Facebook groups regarding
socio-economic and political issues, and we made efforts to extract the basic
emotions (sadness, happiness, disgust, surprise, fear, anger) conveyed through
these comments. Finally, we compared the results of the five most popular
classical machine learning techniques namely Naive Bayes, Decision Tree,
k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and K-Means Clustering
with several combinations of features. Our best model (SVM with a non-linear
radial-basis function (RBF) kernel) achieved an overall average accuracy score
of 52.98% and an F1 score (macro) of 0.3324
| 2,019 | Computation and Language |
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb
Constructions? | There is a growing interest in investigating what neural NLP models learn
about language. A prominent open question is the question of whether or not it
is necessary to model hierarchical structure. We present a linguistic
investigation of a neural parser adding insights to this question. We look at
transitivity and agreement information of auxiliary verb constructions (AVCs)
in comparison to finite main verbs (FMVs). This comparison is motivated by
theoretical work in dependency grammar and in particular the work of Tesni\`ere
(1959) where AVCs and FMVs are both instances of a nucleus, the basic unit of
syntax. An AVC is a dissociated nucleus, it consists of at least two words, and
an FMV is its non-dissociated counterpart, consisting of exactly one word. We
suggest that the representation of AVCs and FMVs should capture similar
information. We use diagnostic classifiers to probe agreement and transitivity
information in vectors learned by a transition-based neural parser in four
typologically different languages. We find that the parser learns different
information about AVCs and FMVs if only sequential models (BiLSTMs) are used in
the architecture but similar information when a recursive layer is used. We
find explanations for why this is the case by looking closely at how
information is learned in the network and looking at what happens with
different dependency representations of AVCs. We conclude that there may be
benefits to using a recursive layer in dependency parsing and that we have not
yet found the best way to integrate it in our parsers.
| 2,020 | Computation and Language |
Deep Neural Models for Medical Concept Normalization in User-Generated
Texts | In this work, we consider the medical concept normalization problem, i.e.,
the problem of mapping a health-related entity mention in a free-form text to a
concept in a controlled vocabulary, usually to the standard thesaurus in the
Unified Medical Language System (UMLS). This is a challenging task since
medical terminology is very different when coming from health care
professionals or from the general public in the form of social media texts. We
approach it as a sequence learning problem with powerful neural networks such
as recurrent neural networks and contextualized word representation models
trained to obtain semantic representations of social media expressions. Our
experimental evaluation over three different benchmarks shows that neural
architectures leverage the semantic meaning of the entity mention and
significantly outperform an existing state of the art models.
| 2,019 | Computation and Language |
Understanding Neural Machine Translation by Simplification: The Case of
Encoder-free Models | In this paper, we try to understand neural machine translation (NMT) via
simplifying NMT architectures and training encoder-free NMT models. In an
encoder-free model, the sums of word embeddings and positional embeddings
represent the source. The decoder is a standard Transformer or recurrent neural
network that directly attends to embeddings via attention mechanisms.
Experimental results show (1) that the attention mechanism in encoder-free
models acts as a strong feature extractor, (2) that the word embeddings in
encoder-free models are competitive to those in conventional models, (3) that
non-contextualized source representations lead to a big performance drop, and
(4) that encoder-free models have different effects on alignment quality for
German-English and Chinese-English.
| 2,019 | Computation and Language |
OCC: A Smart Reply System for Efficient In-App Communications | Smart reply systems have been developed for various messaging platforms. In
this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which
is a key enhanced feature on top of the Uber in-app chat system. It enables
driver-partners to quickly respond to rider messages using smart replies. The
smart replies are dynamically selected according to conversation content using
machine learning algorithms. Our system consists of two major components:
intent detection and reply retrieval, which are very different from standard
smart reply systems where the task is to directly predict a reply. It is
designed specifically for mobile applications with short and non-canonical
messages. Reply retrieval utilizes pairings between intent and reply based on
their popularity in chat messages as derived from historical data. For intent
detection, a set of embedding and classification techniques are experimented
with, and we choose to deploy a solution using unsupervised distributed
embedding and nearest-neighbor classifier. It has the advantage of only
requiring a small amount of labeled training data, simplicity in developing and
deploying to production, and fast inference during serving and hence highly
scalable. At the same time, it performs comparably with deep learning
architectures such as word-level convolutional neural network. Overall, the
system achieves a high accuracy of 76% on intent detection. Currently, the
system is deployed in production for English-speaking countries and 71% of
in-app communications between riders and driver-partners adopted the smart
replies to speedup the communication process.
| 2,019 | Computation and Language |
Querying Knowledge via Multi-Hop English Questions | The inherent difficulty of knowledge specification and the lack of trained
specialists are some of the key obstacles on the way to making intelligent
systems based on the knowledge representation and reasoning (KRR) paradigm
commonplace. Knowledge and query authoring using natural language, especially
controlled natural language (CNL), is one of the promising approaches that
could enable domain experts, who are not trained logicians, to both create
formal knowledge and query it. In previous work, we introduced the KALM system
(Knowledge Authoring Logic Machine) that supports knowledge authoring (and
simple querying) with very high accuracy that at present is unachievable via
machine learning approaches. The present paper expands on the question
answering aspect of KALM and introduces KALM-QA (KALM for Question Answering)
that is capable of answering much more complex English questions. We show that
KALM-QA achieves 100% accuracy on an extensive suite of movie-related
questions, called MetaQA, which contains almost 29,000 test questions and over
260,000 training questions. We contrast this with a published machine learning
approach, which falls far short of this high mark.
| 2,019 | Computation and Language |
Evaluating the Utility of Document Embedding Vector Difference for
Relation Learning | Recent work has demonstrated that vector offsets obtained by subtracting
pretrained word embedding vectors can be used to predict lexical relations with
surprising accuracy. Inspired by this finding, in this paper, we extend the
idea to the document level, in generating document-level embeddings,
calculating the distance between them, and using a linear classifier to
classify the relation between the documents. In the context of duplicate
detection and dialogue act tagging tasks, we show that document-level
difference vectors have utility in assessing document-level similarity, but
perform less well in multi-relational classification.
| 2,019 | Computation and Language |
SLATE: A Super-Lightweight Annotation Tool for Experts | Many annotation tools have been developed, covering a wide variety of tasks
and providing features like user management, pre-processing, and automatic
labeling. However, all of these tools use Graphical User Interfaces, and often
require substantial effort to install and configure. This paper presents a new
annotation tool that is designed to fill the niche of a lightweight interface
for users with a terminal-based workflow. Slate supports annotation at
different scales (spans of characters, tokens, and lines, or a document) and of
different types (free text, labels, and links), with easily customisable
keybindings, and unicode support. In a user study comparing with other tools it
was consistently the easiest to install and use. Slate fills a need not met by
existing systems, and has already been used to annotate two corpora, one of
which involved over 250 hours of annotation effort.
| 2,019 | Computation and Language |
Joint Learning of Named Entity Recognition and Entity Linking | Named entity recognition (NER) and entity linking (EL) are two fundamentally
related tasks, since in order to perform EL, first the mentions to entities
have to be detected. However, most entity linking approaches disregard the
mention detection part, assuming that the correct mentions have been previously
detected. In this paper, we perform joint learning of NER and EL to leverage
their relatedness and obtain a more robust and generalisable system. For that,
we introduce a model inspired by the Stack-LSTM approach (Dyer et al., 2015).
We observe that, in fact, doing multi-task learning of NER and EL improves the
performance in both tasks when comparing with models trained with individual
objectives. Furthermore, we achieve results competitive with the
state-of-the-art in both NER and EL.
| 2,019 | Computation and Language |
Exploring sentence informativeness | This study is a preliminary exploration of the concept of informativeness
-how much information a sentence gives about a word it contains- and its
potential benefits to building quality word representations from scarce data.
We propose several sentence-level classifiers to predict informativeness, and
we perform a manual annotation on a set of sentences. We conclude that these
two measures correspond to different notions of informativeness. However, our
experiments show that using the classifiers' predictions to train word
embeddings has an impact on embedding quality.
| 2,019 | Computation and Language |
Multi-Granular Text Encoding for Self-Explaining Categorization | Self-explaining text categorization requires a classifier to make a
prediction along with supporting evidence. A popular type of evidence is
sub-sequences extracted from the input text which are sufficient for the
classifier to make the prediction. In this work, we define multi-granular
ngrams as basic units for explanation, and organize all ngrams into a
hierarchical structure, so that shorter ngrams can be reused while computing
longer ngrams. We leverage a tree-structured LSTM to learn a
context-independent representation for each unit via parameter sharing.
Experiments on medical disease classification show that our model is more
accurate, efficient and compact than BiLSTM and CNN baselines. More
importantly, our model can extract intuitive multi-granular evidence to support
its predictions.
| 2,019 | Computation and Language |
Predicting Human Activities from User-Generated Content | The activities we do are linked to our interests, personality, political
preferences, and decisions we make about the future. In this paper, we explore
the task of predicting human activities from user-generated content. We collect
a dataset containing instances of social media users writing about a range of
everyday activities. We then use a state-of-the-art sentence embedding
framework tailored to recognize the semantics of human activities and perform
an automatic clustering of these activities. We train a neural network model to
make predictions about which clusters contain activities that were performed by
a given user based on the text of their previous posts and self-description.
Additionally, we explore the degree to which incorporating inferred user traits
into our model helps with this prediction task.
| 2,019 | Computation and Language |
A Pragmatics-Centered Evaluation Framework for Natural Language
Understanding | New models for natural language understanding have recently made an
unparalleled amount of progress, which has led some researchers to suggest that
the models induce universal text representations. However, current benchmarks
are predominantly targeting semantic phenomena; we make the case that
pragmatics needs to take center stage in the evaluation of natural language
understanding. We introduce PragmEval, a new benchmark for the evaluation of
natural language understanding, that unites 11 pragmatics-focused evaluation
datasets for English. PragmEval can be used as supplementary training data in a
multi-task learning setup, and is publicly available, alongside the code for
gathering and preprocessing the datasets. Using our evaluation suite, we show
that natural language inference, a widely used pretraining task, does not
result in genuinely universal representations, which presents a new challenge
for multi-task learning.
| 2,022 | Computation and Language |
What is this Article about? Extreme Summarization with Topic-aware
Convolutional Neural Networks | We introduce 'extreme summarization', a new single-document summarization
task which aims at creating a short, one-sentence news summary answering the
question ``What is the article about?''. We argue that extreme summarization,
by nature, is not amenable to extractive strategies and requires an abstractive
modeling approach. In the hope of driving research on this task further: (a) we
collect a real-world, large scale dataset by harvesting online articles from
the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive
model which is conditioned on the article's topics and based entirely on
convolutional neural networks. We demonstrate experimentally that this
architecture captures long-range dependencies in a document and recognizes
pertinent content, outperforming an oracle extractive system and
state-of-the-art abstractive approaches when evaluated automatically and by
humans on the extreme summarization dataset.
| 2,019 | Computation and Language |
Incremental Transformer with Deliberation Decoder for Document Grounded
Conversations | Document Grounded Conversations is a task to generate dialogue responses when
chatting about the content of a given document. Obviously, document knowledge
plays a critical role in Document Grounded Conversations, while existing
dialogue models do not exploit this kind of knowledge effectively enough. In
this paper, we propose a novel Transformer-based architecture for multi-turn
document grounded conversations. In particular, we devise an Incremental
Transformer to encode multi-turn utterances along with knowledge in related
documents. Motivated by the human cognitive process, we design a two-pass
decoder (Deliberation Decoder) to improve context coherence and knowledge
correctness. Our empirical study on a real-world Document Grounded Dataset
proves that responses generated by our model significantly outperform
competitive baselines on both context coherence and knowledge relevance.
| 2,019 | Computation and Language |
The Unbearable Weight of Generating Artificial Errors for Grammatical
Error Correction | In recent years, sequence-to-sequence models have been very effective for
end-to-end grammatical error correction (GEC). As creating human-annotated
parallel corpus for GEC is expensive and time-consuming, there has been work on
artificial corpus generation with the aim of creating sentences that contain
realistic grammatical errors from grammatically correct sentences. In this
paper, we investigate the impact of using recent neural models for generating
errors to help neural models to correct errors. We conduct a battery of
experiments on the effect of data size, models, and comparison with a
rule-based approach.
| 2,019 | Computation and Language |
Using Word Embeddings to Examine Gender Bias in Dutch Newspapers,
1950-1990 | Contemporary debates on filter bubbles and polarization in public and social
media raise the question to what extent news media of the past exhibited
biases. This paper specifically examines bias related to gender in six Dutch
national newspapers between 1950 and 1990. We measure bias related to gender by
comparing local changes in word embedding models trained on newspapers with
divergent ideological backgrounds. We demonstrate clear differences in gender
bias and changes within and between newspapers over time. In relation to themes
such as sexuality and leisure, we see the bias moving toward women, whereas,
generally, the bias shifts in the direction of men, despite growing female
employment number and feminist movements. Even though Dutch society became less
stratified ideologically (depillarization), we found an increasing divergence
in gender bias between religious and social-democratic on the one hand and
liberal newspapers on the other. Methodologically, this paper illustrates how
word embeddings can be used to examine historical language change. Future work
will investigate how fine-tuning deep contextualized embedding models, such as
ELMO, might be used for similar tasks with greater contextual information.
| 2,019 | Computation and Language |
Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine
Translation | Visual Genome is a dataset connecting structured image information with
English language. We present ``Hindi Visual Genome'', a multimodal dataset
consisting of text and images suitable for English-Hindi multimodal machine
translation task and multimodal research. We have selected short English
segments (captions) from Visual Genome along with associated images and
automatically translated them to Hindi with manual post-editing which took the
associated images into account. We prepared a set of 31525 segments,
accompanied by a challenge test set of 1400 segments. This challenge test set
was created by searching for (particularly) ambiguous English words based on
the embedding similarity and manually selecting those where the image helps to
resolve the ambiguity.
Our dataset is the first for multimodal English-Hindi machine translation,
freely available for non-commercial research purposes. Our Hindi version of
Visual Genome also allows to create Hindi image labelers or other practical
tools.
Hindi Visual Genome also serves in Workshop on Asian Translation (WAT) 2019
Multi-Modal Translation Task.
| 2,019 | Computation and Language |
Augmenting a BiLSTM tagger with a Morphological Lexicon and a Lexical
Category Identification Step | Previous work on using BiLSTM models for PoS tagging has primarily focused on
small tagsets. We evaluate BiLSTM models for tagging Icelandic, a
morphologically rich language, using a relatively large tagset. Our baseline
BiLSTM model achieves higher accuracy than any previously published tagger not
taking advantage of a morphological lexicon. When we extend the model by
incorporating such data, we outperform previous state-of-the-art results by a
significant margin. We also report on work in progress that attempts to address
the problem of data sparsity inherent in morphologically detailed, fine-grained
tagsets. We experiment with training a separate model on only the lexical
category and using the coarse-grained output tag as an input for the main
model. This method further increases the accuracy and reduces the tagging
errors by 21.3% compared to previous state-of-the-art results. Finally, we
train and test our tagger on a new gold standard for Icelandic.
| 2,019 | Computation and Language |
Learning dynamic word embeddings with drift regularisation | Word usage, meaning and connotation change throughout time. Diachronic word
embeddings are used to grasp these changes in an unsupervised way. In this
paper, we use variants of the Dynamic Bernoulli Embeddings model to learn
dynamic word embeddings, in order to identify notable properties of the model.
The comparison is made on the New York Times Annotated Corpus in English and a
set of articles from the French newspaper Le Monde covering the same period.
This allows us to define a pipeline to analyse the evolution of words use
across two languages.
| 2,019 | Computation and Language |
Generating Sentiment-Preserving Fake Online Reviews Using Neural
Language Models and Their Human- and Machine-based Detection | Advanced neural language models (NLMs) are widely used in sequence generation
tasks because they are able to produce fluent and meaningful sentences. They
can also be used to generate fake reviews, which can then be used to attack
online review systems and influence the buying decisions of online shoppers. To
perform such attacks, it is necessary for experts to train a tailored LM for a
specific topic. In this work, we show that a low-skilled threat model can be
built just by combining publicly available LMs and show that the produced fake
reviews can fool both humans and machines. In particular, we use the GPT-2 NLM
to generate a large number of high-quality reviews based on a review with the
desired sentiment and then using a BERT based text classifier (with accuracy of
96%) to filter out reviews with undesired sentiments. Because none of the words
in the review are modified, fluent samples like the training data can be
generated from the learned distribution. A subjective evaluation with 80
participants demonstrated that this simple method can produce reviews that are
as fluent as those written by people. It also showed that the participants
tended to distinguish fake reviews randomly. Three countermeasures, Grover,
GLTR, and OpenAI GPT-2 detector, were found to be difficult to accurately
detect fake review.
| 2,019 | Computation and Language |
ELI5: Long Form Question Answering | We introduce the first large-scale corpus for long-form question answering, a
task requiring elaborate and in-depth answers to open-ended questions. The
dataset comprises 270K threads from the Reddit forum ``Explain Like I'm Five''
(ELI5) where an online community provides answers to questions which are
comprehensible by five year olds. Compared to existing datasets, ELI5 comprises
diverse questions requiring multi-sentence answers. We provide a large set of
web documents to help answer the question. Automatic and human evaluations show
that an abstractive model trained with a multi-task objective outperforms
conventional Seq2Seq, language modeling, as well as a strong extractive
baseline. However, our best model is still far from human performance since
raters prefer gold responses in over 86% of cases, leaving ample opportunity
for future improvement.
| 2,019 | Computation and Language |
Syntax-aware Neural Semantic Role Labeling | Semantic role labeling (SRL), also known as shallow semantic parsing, is an
important yet challenging task in NLP. Motivated by the close correlation
between syntactic and semantic structures, traditional discrete-feature-based
SRL approaches make heavy use of syntactic features. In contrast,
deep-neural-network-based approaches usually encode the input sentence as a
word sequence without considering the syntactic structures. In this work, we
investigate several previous approaches for encoding syntactic trees, and make
a thorough study on whether extra syntax-aware representations are beneficial
for neural SRL models. Experiments on the benchmark CoNLL-2005 dataset show
that syntax-aware SRL approaches can effectively improve performance over a
strong baseline with external word representations from ELMo. With the extra
syntax-aware representations, our approaches achieve new state-of-the-art 85.6
F1 (single model) and 86.6 F1 (ensemble) on the test data, outperforming the
corresponding strong baselines with ELMo by 0.8 and 1.0, respectively. Detailed
error analysis are conducted to gain more insights on the investigated
approaches.
| 2,019 | Computation and Language |
VIFIDEL: Evaluating the Visual Fidelity of Image Descriptions | We address the task of evaluating image description generation systems. We
propose a novel image-aware metric for this task: VIFIDEL. It estimates the
faithfulness of a generated caption with respect to the content of the actual
image, based on the semantic similarity between labels of objects depicted in
images and words in the description. The metric is also able to take into
account the relative importance of objects mentioned in human reference
descriptions during evaluation. Even if these human reference descriptions are
not available, VIFIDEL can still reliably evaluate system descriptions. The
metric achieves high correlation with human judgments on two well-known
datasets and is competitive with metrics that depend on human references
| 2,019 | Computation and Language |
Introduction to Neural Network based Approaches for Question Answering
over Knowledge Graphs | Question answering has emerged as an intuitive way of querying structured
data sources, and has attracted significant advancements over the years. In
this article, we provide an overview over these recent advancements, focusing
on neural network based question answering systems over knowledge graphs. We
introduce readers to the challenges in the tasks, current paradigms of
approaches, discuss notable advancements, and outline the emerging trends in
the field. Through this article, we aim to provide newcomers to the field with
a suitable entry point, and ease their process of making informed decisions
while creating their own QA system.
| 2,019 | Computation and Language |
Emotion Detection in Text: Focusing on Latent Representation | In recent years, emotion detection in text has become more popular due to its
vast potential applications in marketing, political science, psychology,
human-computer interaction, artificial intelligence, etc. In this work, we
argue that current methods which are based on conventional machine learning
models cannot grasp the intricacy of emotional language by ignoring the
sequential nature of the text, and the context. These methods, therefore, are
not sufficient to create an applicable and generalizable emotion detection
methodology. Understanding these limitations, we present a new network based on
a bidirectional GRU model to show that capturing more meaningful information
from text can significantly improve the performance of these models. The
results show significant improvement with an average of 26.8 point increase in
F-measure on our test data and 38.6 increase on the totally new dataset.
| 2,019 | Computation and Language |
Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality
Variation and Discourse Contrast | Neural generation methods for task-oriented dialogue typically generate from
a meaning representation that is populated using a database of domain
information, such as a table of data describing a restaurant. While earlier
work focused solely on the semantic fidelity of outputs, recent work has
started to explore methods for controlling the style of the generated text
while simultaneously achieving semantic accuracy. Here we experiment with two
stylistic benchmark tasks, generating language that exhibits variation in
personality, and generating discourse contrast. We report a huge performance
improvement in both stylistic control and semantic accuracy over the state of
the art on both of these benchmarks. We test several different models and show
that putting stylistic conditioning in the decoder and eliminating the semantic
re-ranker used in earlier models results in more than 15 points higher BLEU for
Personality, with a reduction of semantic error to near zero. We also report an
improvement from .75 to .81 in controlling contrast and a reduction in semantic
error from 16% to 2%.
| 2,019 | Computation and Language |
On Modeling ASR Word Confidence | We present a new method for computing ASR word confidences that effectively
mitigates the effect of ASR errors for diverse downstream applications,
improves the word error rate of the 1-best result, and allows better comparison
of scores across different models. We propose 1) a new method for modeling word
confidence using a Heterogeneous Word Confusion Network (HWCN) that addresses
some key flaws in conventional Word Confusion Networks, and 2) a new score
calibration method for facilitating direct comparison of scores from different
models. Using a bidirectional lattice recurrent neural network to compute the
confidence scores of each word in the HWCN, we show that the word sequence with
the best overall confidence is more accurate than the default 1-best result of
the recognizer, and that the calibration method can substantially improve the
reliability of recognizer combination.
| 2,020 | Computation and Language |
EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification | This paper describes our approach to the EmotionX-2019, the shared task of
SocialNLP 2019. To detect emotion for each utterance of two datasets from the
TV show Friends and Facebook chat log EmotionPush, we propose two-step deep
learning based methodology: (i) encode each of the utterance into a sequence of
vectors that represent its meaning; and (ii) use a simply softmax classifier to
predict one of the emotions amongst four candidates that an utterance may
carry. Notice that the source of labeled utterances is not rich, we utilise a
well-trained model, known as BERT, to transfer part of the knowledge learned
from a large amount of corpus to our model. We then focus on fine-tuning our
model until it well fits to the in-domain data. The performance of the proposed
model is evaluated by micro-F1 scores, i.e., 79.1% and 86.2% for the testsets
of Friends and EmotionPush, respectively. Our model ranks 3rd among 11
submissions.
| 2,019 | Computation and Language |
Pre-Learning Environment Representations for Data-Efficient Neural
Instruction Following | We consider the problem of learning to map from natural language instructions
to state transitions (actions) in a data-efficient manner. Our method takes
inspiration from the idea that it should be easier to ground language to
concepts that have already been formed through pre-linguistic observation. We
augment a baseline instruction-following learner with an initial
environment-learning phase that uses observations of language-free state
transitions to induce a suitable latent representation of actions before
processing the instruction-following training data. We show that mapping to
pre-learned representations substantially improves performance over systems
whose representations are learned from limited instructional data alone.
| 2,019 | Computation and Language |
Discourse Marker Augmented Network with Reinforcement Learning for
Natural Language Inference | Natural Language Inference (NLI), also known as Recognizing Textual
Entailment (RTE), is one of the most important problems in natural language
processing. It requires to infer the logical relationship between two given
sentences. While current approaches mostly focus on the interaction
architectures of the sentences, in this paper, we propose to transfer knowledge
from some important discourse markers to augment the quality of the NLI model.
We observe that people usually use some discourse markers such as "so" or "but"
to represent the logical relationship between two sentences. These words
potentially have deep connections with the meanings of the sentences, thus can
be utilized to help improve the representations of them. Moreover, we use
reinforcement learning to optimize a new objective function with a reward
defined by the property of the NLI datasets to make full use of the labels
information. Experiments show that our method achieves the state-of-the-art
performance on several large-scale datasets.
| 2,019 | Computation and Language |
Learning to Select, Track, and Generate for Data-to-Text | We propose a data-to-text generation model with two modules, one for tracking
and the other for text generation. Our tracking module selects and keeps track
of salient information and memorizes which record has been mentioned. Our
generation module generates a summary conditioned on the state of tracking
module. Our model is considered to simulate the human-like writing process that
gradually selects the information by determining the intermediate variables
while writing the summary. In addition, we also explore the effectiveness of
the writer information for generation. Experimental results show that our model
outperforms existing models in all evaluation metrics even without writer
information. Incorporating writer information further improves the performance,
contributing to content planning and surface realization.
| 2,021 | Computation and Language |
Towards Unsupervised Grammatical Error Correction using Statistical
Machine Translation with Synthetic Comparable Corpus | We introduce unsupervised techniques based on phrase-based statistical
machine translation for grammatical error correction (GEC) trained on a pseudo
learner corpus created by Google Translation. We verified our GEC system
through experiments on various GEC dataset, includi ng a low resource track of
the shared task at Building Educational Applications 2019 (BEA 2019). As a
result, we achieved an F_0.5 score of 28.31 points with the test data of the
low resource track.
| 2,019 | Computation and Language |
Position Focused Attention Network for Image-Text Matching | Image-text matching tasks have recently attracted a lot of attention in the
computer vision field. The key point of this cross-domain problem is how to
accurately measure the similarity between the visual and the textual contents,
which demands a fine understanding of both modalities. In this paper, we
propose a novel position focused attention network (PFAN) to investigate the
relation between the visual and the textual views. In this work, we integrate
the object position clue to enhance the visual-text joint-embedding learning.
We first split the images into blocks, by which we infer the relative position
of region in the image. Then, an attention mechanism is proposed to model the
relations between the image region and blocks and generate the valuable
position feature, which will be further utilized to enhance the region
expression and model a more reliable relationship between the visual image and
the textual sentence. Experiments on the popular datasets Flickr30K and MS-COCO
show the effectiveness of the proposed method. Besides the public datasets, we
also conduct experiments on our collected practical large-scale news dataset
(Tencent-News) to validate the practical application value of proposed method.
As far as we know, this is the first attempt to test the performance on the
practical application. Our method achieves the state-of-art performance on all
of these three datasets.
| 2,019 | Computation and Language |
Overview and Results: CL-SciSumm Shared Task 2019 | The CL-SciSumm Shared Task is the first medium-scale shared task on
scientific document summarization in the computational linguistics~(CL) domain.
In 2019, it comprised three tasks: (1A) identifying relationships between
citing documents and the referred document, (1B) classifying the discourse
facets, and (2) generating the abstractive summary. The dataset comprised 40
annotated sets of citing and reference papers of the CL-SciSumm 2018 corpus and
1000 more from the SciSummNet dataset. All papers are from the open access
research papers in the CL domain. This overview describes the participation and
the official results of the CL-SciSumm 2019 Shared Task, organized as a part of
the 42nd Annual Conference of the Special Interest Group in Information
Retrieval (SIGIR), held in Paris, France in July 2019. We compare the
participating systems in terms of two evaluation metrics and discuss the use of
ROUGE as an evaluation metric. The annotated dataset used for this shared task
and the scripts used for evaluation can be accessed and used by the community
at: https://github.com/WING-NUS/scisumm-corpus.
| 2,019 | Computation and Language |
Modeling question asking using neural program generation | People ask questions that are far richer, more informative, and more creative
than current AI systems. We propose a neuro-symbolic framework for modeling
human question asking, which represents questions as formal programs and
generates programs with an encoder-decoder based deep neural network. From
extensive experiments using an information-search game, we show that our method
can predict which questions humans are likely to ask in unconstrained settings.
We also propose a novel grammar-based question generation framework trained
with reinforcement learning, which is able to generate creative questions
without supervised human data.
| 2,021 | Computation and Language |
Structured Fusion Networks for Dialog | Neural dialog models have exhibited strong performance, however their
end-to-end nature lacks a representation of the explicit structure of dialog.
This results in a loss of generalizability, controllability and a data-hungry
nature. Conversely, more traditional dialog systems do have strong models of
explicit structure. This paper introduces several approaches for explicitly
incorporating structure into neural models of dialog. Structured Fusion
Networks first learn neural dialog modules corresponding to the structured
components of traditional dialog systems and then incorporate these modules in
a higher-level generative model. Structured Fusion Networks obtain strong
results on the MultiWOZ dataset, both with and without reinforcement learning.
Structured Fusion Networks are shown to have several valuable properties,
including better domain generalizability, improved performance in reduced data
scenarios and robustness to divergence during reinforcement learning.
| 2,019 | Computation and Language |
Happiness Entailment: Automating Suggestions for Well-Being | Understanding what makes people happy is a central topic in psychology. Prior
work has mostly focused on developing self-reporting assessment tools for
individuals and relies on experts to analyze the periodic reported assessments.
One of the goals of the analysis is to understand what actions are necessary to
encourage modifications in the behaviors of the individuals to improve their
overall well-being. In this paper, we outline a complementary approach; on the
assumption that the user journals her happy moments as short texts, a system
can analyze these texts and propose sustainable suggestions for the user that
may lead to an overall improvement in her well-being. We prototype one
necessary component of such a system, the Happiness Entailment Recognition
(HER) module, which takes as input a short text describing an event, a
candidate suggestion, and outputs a determination about whether the suggestion
is more likely to be good for this user based on the event described. This
component is implemented as a neural network model with two encoders, one for
the user input and one for the candidate actionable suggestion, with additional
layers to capture psychologically significant features in the happy moment and
suggestion.
| 2,019 | Computation and Language |
CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context
in Morphology | This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019
task 2 of Morphological Analysis and Lemmatization in Context. This task
requires us to produce the lemma and morpho-syntactic description of each token
in a sequence, for 107 treebanks. We approach this task with a hierarchical
neural conditional random field (CRF) model which predicts each coarse-grained
feature (eg. POS, Case, etc.) independently. However, most treebanks are
under-resourced, thus making it challenging to train deep neural models for
them. Hence, we propose a multi-lingual transfer training regime where we
transfer from multiple related languages that share similar typology.
| 2,019 | Computation and Language |
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question
Entailment using contextualized representations | This paper presents the submissions by Team Dr.Quad to the ACL-BioNLP 2019
shared task on Textual Inference and Question Entailment in the Medical Domain.
Our system is based on the prior work Liu et al. (2019) which uses a multi-task
objective function for textual entailment. In this work, we explore different
strategies for generalizing state-of-the-art language understanding models to
the specialized medical domain. Our results on the shared task demonstrate that
incorporating domain knowledge through data augmentation is a powerful strategy
for addressing challenges posed by specialized domains such as medicine.
| 2,019 | Computation and Language |
Fine-Grained Sentence Functions for Short-Text Conversation | Sentence function is an important linguistic feature referring to a user's
purpose in uttering a specific sentence. The use of sentence function has shown
promising results to improve the performance of conversation models. However,
there is no large conversation dataset annotated with sentence functions. In
this work, we collect a new Short-Text Conversation dataset with manually
annotated SEntence FUNctions (STC-Sefun). Classification models are trained on
this dataset to (i) recognize the sentence function of new data in a large
corpus of short-text conversations; (ii) estimate a proper sentence function of
the response given a test query. We later train conversation models conditioned
on the sentence functions, including information retrieval-based and neural
generative models. Experimental results demonstrate that the use of sentence
functions can help improve the quality of the returned responses.
| 2,019 | Computation and Language |
Unbabel's Participation in the WMT19 Translation Quality Estimation
Shared Task | We present the contribution of the Unbabel team to the WMT 2019 Shared Task
on Quality Estimation. We participated on the word, sentence, and
document-level tracks, encompassing 3 language pairs: English-German,
English-Russian, and English-French. Our submissions build upon the recent
OpenKiwi framework: we combine linear, neural, and predictor-estimator systems
with new transfer learning approaches using BERT and XLM pre-trained models. We
compare systems individually and propose new ensemble techniques for word and
sentence-level predictions. We also propose a simple technique for converting
word labels into document-level predictions. Overall, our submitted systems
achieve the best results on all tracks and language pairs by a considerable
margin.
| 2,019 | Computation and Language |
Translator2Vec: Understanding and Representing Human Post-Editors | The combination of machines and humans for translation is effective, with
many studies showing productivity gains when humans post-edit
machine-translated output instead of translating from scratch. To take full
advantage of this combination, we need a fine-grained understanding of how
human translators work, and which post-editing styles are more effective than
others. In this paper, we release and analyze a new dataset with document-level
post-editing action sequences, including edit operations from keystrokes, mouse
actions, and waiting times. Our dataset comprises 66,268 full document sessions
post-edited by 332 humans, the largest of the kind released to date. We show
that action sequences are informative enough to identify post-editors
accurately, compared to baselines that only look at the initial and final text.
We build on this to learn and visualize continuous representations of
post-editors, and we show that these representations improve the downstream
task of predicting post-editing time.
| 2,019 | Computation and Language |
Automatic Generation of Personalized Comment Based on User Profile | Comments on social media are very diverse, in terms of content, style and
vocabulary, which make generating comments much more challenging than other
existing natural language generation~(NLG) tasks. Besides, since different user
has different expression habits, it is necessary to take the user's profile
into consideration when generating comments. In this paper, we introduce the
task of automatic generation of personalized comment~(AGPC) for social media.
Based on tens of thousands of users' real comments and corresponding user
profiles on weibo, we propose Personalized Comment Generation Network~(PCGN)
for AGPC. The model utilizes user feature embedding with a gated memory and
attends to user description to model personality of users. In addition,
external user representation is taken into consideration during the decoding to
enhance the comments generation. Experimental results show that our model can
generate natural, human-like and personalized comments.
| 2,019 | Computation and Language |
Distributional Analysis of Polysemous Function Words | In this paper, we are concerned with the phenomenon of function word
polysemy. We adopt the framework of distributional semantics, which
characterizes word meaning by observing occurrence contexts in large corpora
and which is in principle well situated to model polysemy. Nevertheless,
function words were traditionally considered as impossible to analyze
distributionally due to their highly flexible usage patterns. We establish that
contextualized word embeddings, the most recent generation of distributional
methods, offer hope in this regard. Using the German reflexive pronoun 'sich'
as an example, we find that contextualized word embeddings capture
theoretically motivated word senses for 'sich' to the extent to which these
senses are mirrored systematically in linguistic usage.
| 2,021 | Computation and Language |
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