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Character-Level Feature Extraction with Densely Connected Networks | Generating character-level features is an important step for achieving good
results in various natural language processing tasks. To alleviate the need for
human labor in generating hand-crafted features, methods that utilize neural
architectures such as Convolutional Neural Network (CNN) or Recurrent Neural
Network (RNN) to automatically extract such features have been proposed and
have shown great results. However, CNN generates position-independent features,
and RNN is slow since it needs to process the characters sequentially. In this
paper, we propose a novel method of using a densely connected network to
automatically extract character-level features. The proposed method does not
require any language or task specific assumptions, and shows robustness and
effectiveness while being faster than CNN- or RNN-based methods. Evaluating
this method on three sequence labeling tasks - slot tagging, Part-of-Speech
(POS) tagging, and Named-Entity Recognition (NER) - we obtain state-of-the-art
performance with a 96.62 F1-score and 97.73% accuracy on slot tagging and POS
tagging, respectively, and comparable performance to the state-of-the-art 91.13
F1-score on NER.
| 2,018 | Computation and Language |
Modeling Multi-turn Conversation with Deep Utterance Aggregation | Multi-turn conversation understanding is a major challenge for building
intelligent dialogue systems. This work focuses on retrieval-based response
matching for multi-turn conversation whose related work simply concatenates the
conversation utterances, ignoring the interactions among previous utterances
for context modeling. In this paper, we formulate previous utterances into
context using a proposed deep utterance aggregation model to form a
fine-grained context representation. In detail, a self-matching attention is
first introduced to route the vital information in each utterance. Then the
model matches a response with each refined utterance and the final matching
score is obtained after attentive turns aggregation. Experimental results show
our model outperforms the state-of-the-art methods on three multi-turn
conversation benchmarks, including a newly introduced e-commerce dialogue
corpus.
| 2,018 | Computation and Language |
Subword-augmented Embedding for Cloze Reading Comprehension | Representation learning is the foundation of machine reading comprehension.
In state-of-the-art models, deep learning methods broadly use word and
character level representations. However, character is not naturally the
minimal linguistic unit. In addition, with a simple concatenation of character
and word embedding, previous models actually give suboptimal solution. In this
paper, we propose to use subword rather than character for word embedding
enhancement. We also empirically explore different augmentation strategies on
subword-augmented embedding to enhance the cloze-style reading comprehension
model reader. In detail, we present a reader that uses subword-level
representation to augment word embedding with a short list to handle rare words
effectively. A thorough examination is conducted to evaluate the comprehensive
performance and generalization ability of the proposed reader. Experimental
results show that the proposed approach helps the reader significantly
outperform the state-of-the-art baselines on various public datasets.
| 2,018 | Computation and Language |
One-shot Learning for Question-Answering in Gaokao History Challenge | Answering questions from university admission exams (Gaokao in Chinese) is a
challenging AI task since it requires effective representation to capture
complicated semantic relations between questions and answers. In this work, we
propose a hybrid neural model for deep question-answering task from history
examinations. Our model employs a cooperative gated neural network to retrieve
answers with the assistance of extra labels given by a neural turing machine
labeler. Empirical study shows that the labeler works well with only a small
training dataset and the gated mechanism is good at fetching the semantic
representation of lengthy answers. Experiments on question answering
demonstrate the proposed model obtains substantial performance gains over
various neural model baselines in terms of multiple evaluation metrics.
| 2,018 | Computation and Language |
Fast ASR-free and almost zero-resource keyword spotting using DTW and
CNNs for humanitarian monitoring | We use dynamic time warping (DTW) as supervision for training a convolutional
neural network (CNN) based keyword spotting system using a small set of spoken
isolated keywords. The aim is to allow rapid deployment of a keyword spotting
system in a new language to support urgent United Nations (UN) relief
programmes in parts of Africa where languages are extremely under-resourced and
the development of annotated speech resources is infeasible. First, we use 1920
recorded keywords (40 keyword types, 34 minutes of speech) as exemplars in a
DTW-based template matching system and apply it to untranscribed broadcast
speech. Then, we use the resulting DTW scores as targets to train a CNN on the
same unlabelled speech. In this way we use just 34 minutes of labelled speech,
but leverage a large amount of unlabelled data for training. While the
resulting CNN keyword spotter cannot match the performance of the DTW-based
system, it substantially outperforms a CNN classifier trained only on the
keywords, improving the area under the ROC curve from 0.54 to 0.64. Because our
CNN system is several orders of magnitude faster at runtime than the DTW
system, it represents the most viable keyword spotter on this extremely limited
dataset.
| 2,018 | Computation and Language |
Prior Attention for Style-aware Sequence-to-Sequence Models | We extend sequence-to-sequence models with the possibility to control the
characteristics or style of the generated output, via attention that is
generated a priori (before decoding) from a latent code vector. After training
an initial attention-based sequence-to-sequence model, we use a variational
auto-encoder conditioned on representations of input sequences and a latent
code vector space to generate attention matrices. By sampling the code vector
from specific regions of this latent space during decoding and imposing prior
attention generated from it in the seq2seq model, output can be steered towards
having certain attributes. This is demonstrated for the task of sentence
simplification, where the latent code vector allows control over output length
and lexical simplification, and enables fine-tuning to optimize for different
evaluation metrics.
| 2,018 | Computation and Language |
The Emotional Voices Database: Towards Controlling the Emotion Dimension
in Voice Generation Systems | In this paper, we present a database of emotional speech intended to be
open-sourced and used for synthesis and generation purpose. It contains data
for male and female actors in English and a male actor in French. The database
covers 5 emotion classes so it could be suitable to build synthesis and voice
transformation systems with the potential to control the emotional dimension in
a continuous way. We show the data's efficiency by building a simple MLP system
converting neutral to angry speech style and evaluate it via a CMOS perception
test. Even though the system is a very simple one, the test show the efficiency
of the data which is promising for future work.
| 2,018 | Computation and Language |
Using NLP on news headlines to predict index trends | This paper attempts to provide a state of the art in trend prediction using
news headlines. We present the research done on predicting DJIA trends using
Natural Language Processing. We will explain the different algorithms we have
used as well as the various embedding techniques attempted. We rely on
statistical and deep learning models in order to extract information from the
corpuses.
| 2,018 | Computation and Language |
Neural Machine Translation for Low Resource Languages using Bilingual
Lexicon Induced from Comparable Corpora | Resources for the non-English languages are scarce and this paper addresses
this problem in the context of machine translation, by automatically extracting
parallel sentence pairs from the multilingual articles available on the
Internet. In this paper, we have used an end-to-end Siamese bidirectional
recurrent neural network to generate parallel sentences from comparable
multilingual articles in Wikipedia. Subsequently, we have showed that using the
harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems
for the low-resource language pairs: English--Hindi and English--Tamil, when
compared to training exclusively on the limited bilingual corpora collected for
these language pairs.
| 2,018 | Computation and Language |
A Practical Incremental Learning Framework For Sparse Entity Extraction | This work addresses challenges arising from extracting entities from textual
data, including the high cost of data annotation, model accuracy, selecting
appropriate evaluation criteria, and the overall quality of annotation. We
present a framework that integrates Entity Set Expansion (ESE) and Active
Learning (AL) to reduce the annotation cost of sparse data and provide an
online evaluation method as feedback. This incremental and interactive learning
framework allows for rapid annotation and subsequent extraction of sparse data
while maintaining high accuracy. We evaluate our framework on three publicly
available datasets and show that it drastically reduces the cost of sparse
entity annotation by an average of 85% and 45% to reach 0.9 and 1.0 F-Scores
respectively. Moreover, the method exhibited robust performance across all
datasets.
| 2,018 | Computation and Language |
A Multi-Modal Chinese Poetry Generation Model | Recent studies in sequence-to-sequence learning demonstrate that RNN
encoder-decoder structure can successfully generate Chinese poetry. However,
existing methods can only generate poetry with a given first line or user's
intent theme. In this paper, we proposed a three-stage multi-modal Chinese
poetry generation approach. Given a picture, the first line, the title and the
other lines of the poem are successively generated in three stages. According
to the characteristics of Chinese poems, we propose a hierarchy-attention
seq2seq model which can effectively capture character, phrase, and sentence
information between contexts and improve the symmetry delivered in poems. In
addition, the Latent Dirichlet allocation (LDA) model is utilized for title
generation and improve the relevance of the whole poem and the title. Compared
with strong baseline, the experimental results demonstrate the effectiveness of
our approach, using machine evaluations as well as human judgments.
| 2,019 | Computation and Language |
Unveiling the semantic structure of text documents using paragraph-aware
Topic Models | Classic Topic Models are built under the Bag Of Words assumption, in which
word position is ignored for simplicity. Besides, symmetric priors are
typically used in most applications. In order to easily learn topics with
different properties among the same corpus, we propose a new line of work in
which the paragraph structure is exploited. Our proposal is based on the
following assumption: in many text document corpora there are formal
constraints shared across all the collection, e.g. sections. When this
assumption is satisfied, some paragraphs may be related to general concepts
shared by all documents in the corpus, while others would contain the genuine
description of documents. Assuming each paragraph can be semantically more
general, specific, or hybrid, we look for ways to measure this, transferring
this distinction to topics and being able to learn what we call specific and
general topics. Experiments show that this is a proper methodology to highlight
certain paragraphs in structured documents at the same time we learn
interesting and more diverse topics.
| 2,018 | Computation and Language |
Enhancing Sentence Embedding with Generalized Pooling | Pooling is an essential component of a wide variety of sentence
representation and embedding models. This paper explores generalized pooling
methods to enhance sentence embedding. We propose vector-based multi-head
attention that includes the widely used max pooling, mean pooling, and scalar
self-attention as special cases. The model benefits from properly designed
penalization terms to reduce redundancy in multi-head attention. We evaluate
the proposed model on three different tasks: natural language inference (NLI),
author profiling, and sentiment classification. The experiments show that the
proposed model achieves significant improvement over strong
sentence-encoding-based methods, resulting in state-of-the-art performances on
four datasets. The proposed approach can be easily implemented for more
problems than we discuss in this paper.
| 2,022 | Computation and Language |
Graph-to-Sequence Learning using Gated Graph Neural Networks | Many NLP applications can be framed as a graph-to-sequence learning problem.
Previous work proposing neural architectures on this setting obtained promising
results compared to grammar-based approaches but still rely on linearisation
heuristics and/or standard recurrent networks to achieve the best performance.
In this work, we propose a new model that encodes the full structural
information contained in the graph. Our architecture couples the recently
proposed Gated Graph Neural Networks with an input transformation that allows
nodes and edges to have their own hidden representations, while tackling the
parameter explosion problem present in previous work. Experimental results show
that our model outperforms strong baselines in generation from AMR graphs and
syntax-based neural machine translation.
| 2,018 | Computation and Language |
Conditional Generators of Words Definitions | We explore recently introduced definition modeling technique that provided
the tool for evaluation of different distributed vector representations of
words through modeling dictionary definitions of words. In this work, we study
the problem of word ambiguities in definition modeling and propose a possible
solution by employing latent variable modeling and soft attention mechanisms.
Our quantitative and qualitative evaluation and analysis of the model shows
that taking into account words ambiguity and polysemy leads to performance
improvement.
| 2,018 | Computation and Language |
Neural Cross-Lingual Coreference Resolution and its Application to
Entity Linking | We propose an entity-centric neural cross-lingual coreference model that
builds on multi-lingual embeddings and language-independent features. We
perform both intrinsic and extrinsic evaluations of our model. In the intrinsic
evaluation, we show that our model, when trained on English and tested on
Chinese and Spanish, achieves competitive results to the models trained
directly on Chinese and Spanish respectively. In the extrinsic evaluation, we
show that our English model helps achieve superior entity linking accuracy on
Chinese and Spanish test sets than the top 2015 TAC system without using any
annotated data from Chinese or Spanish.
| 2,018 | Computation and Language |
Contextual Language Model Adaptation for Conversational Agents | Statistical language models (LM) play a key role in Automatic Speech
Recognition (ASR) systems used by conversational agents. These ASR systems
should provide a high accuracy under a variety of speaking styles, domains,
vocabulary and argots. In this paper, we present a DNN-based method to adapt
the LM to each user-agent interaction based on generalized contextual
information, by predicting an optimal, context-dependent set of LM
interpolation weights. We show that this framework for contextual adaptation
provides accuracy improvements under different possible mixture LM partitions
that are relevant for both (1) Goal-oriented conversational agents where it's
natural to partition the data by the requested application and for (2) Non-goal
oriented conversational agents where the data can be partitioned using topic
labels that come from predictions of a topic classifier. We obtain a relative
WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass
decoding framework, over an unadapted model. We also show up to a 15% relative
improvement in recognizing named entities which is of significant value for
conversational ASR systems.
| 2,018 | Computation and Language |
Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural
Network Language Models in ASR | In automatic speech recognition (ASR) systems, recurrent neural network
language models (RNNLM) are used to rescore a word lattice or N-best hypotheses
list. Due to the expensive training, the RNNLM's vocabulary set accommodates
only small shortlist of most frequent words. This leads to suboptimal
performance if an input speech contains many out-of-shortlist (OOS) words. An
effective solution is to increase the shortlist size and retrain the entire
network which is highly inefficient. Therefore, we propose an efficient method
to expand the shortlist set of a pretrained RNNLM without incurring expensive
retraining and using additional training data. Our method exploits the
structure of RNNLM which can be decoupled into three parts: input projection
layer, middle layers, and output projection layer. Specifically, our method
expands the word embedding matrices in projection layers and keeps the middle
layers unchanged. In this approach, the functionality of the pretrained RNNLM
will be correctly maintained as long as OOS words are properly modeled in two
embedding spaces. We propose to model the OOS words by borrowing linguistic
knowledge from appropriate in-shortlist words. Additionally, we propose to
generate the list of OOS words to expand vocabulary in unsupervised manner by
automatically extracting them from ASR output.
| 2,021 | Computation and Language |
Learning Visually-Grounded Semantics from Contrastive Adversarial
Samples | We study the problem of grounding distributional representations of texts on
the visual domain, namely visual-semantic embeddings (VSE for short). Begin
with an insightful adversarial attack on VSE embeddings, we show the limitation
of current frameworks and image-text datasets (e.g., MS-COCO) both
quantitatively and qualitatively. The large gap between the number of possible
constitutions of real-world semantics and the size of parallel data, to a large
extent, restricts the model to establish the link between textual semantics and
visual concepts. We alleviate this problem by augmenting the MS-COCO image
captioning datasets with textual contrastive adversarial samples. These samples
are synthesized using linguistic rules and the WordNet knowledge base. The
construction procedure is both syntax- and semantics-aware. The samples enforce
the model to ground learned embeddings to concrete concepts within the image.
This simple but powerful technique brings a noticeable improvement over the
baselines on a diverse set of downstream tasks, in addition to defending
known-type adversarial attacks. We release the codes at
https://github.com/ExplorerFreda/VSE-C.
| 2,018 | Computation and Language |
Neural Machine Translation for Query Construction and Composition | Research on question answering with knowledge base has recently seen an
increasing use of deep architectures. In this extended abstract, we study the
application of the neural machine translation paradigm for question parsing. We
employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph
query language and their compositions. Instead of inducing the programs through
question-answer pairs, we expect a semi-supervised approach, where alignments
between questions and queries are built through templates. We argue that the
coverage of language utterances can be expanded using late notable works in
natural language generation.
| 2,018 | Computation and Language |
Generalized chart constraints for efficient PCFG and TAG parsing | Chart constraints, which specify at which string positions a constituent may
begin or end, have been shown to speed up chart parsers for PCFGs. We
generalize chart constraints to more expressive grammar formalisms and describe
a neural tagger which predicts chart constraints at very high precision. Our
constraints accelerate both PCFG and TAG parsing, and combine effectively with
other pruning techniques (coarse-to-fine and supertagging) for an overall
speedup of two orders of magnitude, while improving accuracy.
| 2,018 | Computation and Language |
DeepTag: inferring all-cause diagnoses from clinical notes in
under-resourced medical domain | Large scale veterinary clinical records can become a powerful resource for
patient care and research. However, clinicians lack the time and resource to
annotate patient records with standard medical diagnostic codes and most
veterinary visits are captured in free text notes. The lack of standard coding
makes it challenging to use the clinical data to improve patient care. It is
also a major impediment to cross-species translational research, which relies
on the ability to accurately identify patient cohorts with specific diagnostic
criteria in humans and animals. In order to reduce the coding burden for
veterinary clinical practice and aid translational research, we have developed
a deep learning algorithm, DeepTag, which automatically infers diagnostic codes
from veterinary free text notes. DeepTag is trained on a newly curated dataset
of 112,558 veterinary notes manually annotated by experts. DeepTag extends
multi-task LSTM with an improved hierarchical objective that captures the
semantic structures between diseases. To foster human-machine collaboration,
DeepTag also learns to abstain in examples when it is uncertain and defers them
to human experts, resulting in improved performance. DeepTag accurately infers
disease codes from free text even in challenging cross-hospital settings where
the text comes from different clinical settings than the ones used for
training. It enables automated disease annotation across a broad range of
clinical diagnoses with minimal pre-processing. The technical framework in this
work can be applied in other medical domains that currently lack medical coding
resources.
| 2,018 | Computation and Language |
Rich Character-Level Information for Korean Morphological Analysis and
Part-of-Speech Tagging | Due to the fact that Korean is a highly agglutinative, character-rich
language, previous work on Korean morphological analysis typically employs the
use of sub-character features known as graphemes or otherwise utilizes
comprehensive prior linguistic knowledge (i.e., a dictionary of known
morphological transformation forms, or actions). These models have been created
with the assumption that character-level, dictionary-less morphological
analysis was intractable due to the number of actions required. We present, in
this study, a multi-stage action-based model that can perform morphological
transformation and part-of-speech tagging using arbitrary units of input and
apply it to the case of character-level Korean morphological analysis. Among
models that do not employ prior linguistic knowledge, we achieve
state-of-the-art word and sentence-level tagging accuracy with the Sejong
Korean corpus using our proposed data-driven Bi-LSTM model.
| 2,018 | Computation and Language |
Predicting CEFRL levels in learner English on the basis of metrics and
full texts | This paper analyses the contribution of language metrics and, potentially, of
linguistic structures, to classify French learners of English according to
levels of the Common European Framework of Reference for Languages (CEFRL). The
purpose is to build a model for the prediction of learner levels as a function
of language complexity features. We used the EFCAMDAT corpus, a database of one
million written assignments by learners. After applying language complexity
metrics on the texts, we built a representation matching the language metrics
of the texts to their assigned CEFRL levels. Lexical and syntactic metrics were
computed with LCA, LSA, and koRpus. Several supervised learning models were
built by using Gradient Boosted Trees and Keras Neural Network methods and by
contrasting pairs of CEFRL levels. Results show that it is possible to
implement pairwise distinctions, especially for levels ranging from A1 to B1
(A1=>A2: 0.916 AUC and A2=>B1: 0.904 AUC). Model explanation reveals
significant linguistic features for the predictiveness in the corpus. Word
tokens and word types appear to play a significant role in determining levels.
This shows that levels are highly dependent on specific semantic profiles.
| 2,018 | Computation and Language |
Cross-Discourse and Multilingual Exploration of Textual Corpora with the
DualNeighbors Algorithm | Word choice is dependent on the cultural context of writers and their
subjects. Different words are used to describe similar actions, objects, and
features based on factors such as class, race, gender, geography and political
affinity. Exploratory techniques based on locating and counting words may,
therefore, lead to conclusions that reinforce culturally inflected boundaries.
We offer a new method, the DualNeighbors algorithm, for linking thematically
similar documents both within and across discursive and linguistic barriers to
reveal cross-cultural connections. Qualitative and quantitative evaluations of
this technique are shown as applied to two cultural datasets of interest to
researchers across the humanities and social sciences. An open-source
implementation of the DualNeighbors algorithm is provided to assist in its
application.
| 2,018 | Computation and Language |
Neural Machine Translation with Key-Value Memory-Augmented Attention | Although attention-based Neural Machine Translation (NMT) has achieved
remarkable progress in recent years, it still suffers from issues of repeating
and dropping translations. To alleviate these issues, we propose a novel
key-value memory-augmented attention model for NMT, called KVMEMATT.
Specifically, we maintain a timely updated keymemory to keep track of attention
history and a fixed value-memory to store the representation of source sentence
throughout the whole translation process. Via nontrivial transformations and
iterative interactions between the two memories, the decoder focuses on more
appropriate source word(s) for predicting the next target word at each decoding
step, therefore can improve the adequacy of translations. Experimental results
on Chinese=>English and WMT17 German<=>English translation tasks demonstrate
the superiority of the proposed model.
| 2,018 | Computation and Language |
Bias in Semantic and Discourse Interpretation | In this paper, we show how game-theoretic work on conversation combined with
a theory of discourse structure provides a framework for studying interpretive
bias. Interpretive bias is an essential feature of learning and understanding
but also something that can be used to pervert or subvert the truth. The
framework we develop here provides tools for understanding and analyzing the
range of interpretive biases and the factors that contribute to them.
| 2,018 | Computation and Language |
Discourse-Wizard: Discovering Deep Discourse Structure in your
Conversation with RNNs | Spoken language understanding is one of the key factors in a dialogue system,
and a context in a conversation plays an important role to understand the
current utterance. In this work, we demonstrate the importance of context
within the dialogue for neural network models through an online web interface
live demo. We developed two different neural models: a model that does not use
context and a context-based model. The no-context model classifies dialogue
acts at an utterance-level whereas the context-based model takes some preceding
utterances into account. We make these trained neural models available as a
live demo called Discourse-Wizard using a modular server architecture. The live
demo provides an easy to use interface for conversational analysis and for
discovering deep discourse structures in a conversation.
| 2,018 | Computation and Language |
Using General Adversarial Networks for Marketing: A Case Study of Airbnb | In this paper, we examine the use case of general adversarial networks (GANs)
in the field of marketing. In particular, we analyze how GAN models can
replicate text patterns from successful product listings on Airbnb, a
peer-to-peer online market for short-term apartment rentals. To do so, we
define the Diehl-Martinez-Kamalu (DMK) loss function as a new class of
functions that forces the model's generated output to include a set of
user-defined keywords. This allows the general adversarial network to recommend
a way of rewording the phrasing of a listing description to increase the
likelihood that it is booked. Although we tailor our analysis to Airbnb data,
we believe this framework establishes a more general model for how generative
algorithms can be used to produce text samples for the purposes of marketing.
| 2,018 | Computation and Language |
Investigating Speech Features for Continuous Turn-Taking Prediction
Using LSTMs | For spoken dialog systems to conduct fluid conversational interactions with
users, the systems must be sensitive to turn-taking cues produced by a user.
Models should be designed so that effective decisions can be made as to when it
is appropriate, or not, for the system to speak. Traditional end-of-turn
models, where decisions are made at utterance end-points, are limited in their
ability to model fast turn-switches and overlap. A more flexible approach is to
model turn-taking in a continuous manner using RNNs, where the system predicts
speech probability scores for discrete frames within a future window. The
continuous predictions represent generalized turn-taking behaviors observed in
the training data and can be applied to make decisions that are not just
limited to end-of-turn detection. In this paper, we investigate optimal
speech-related feature sets for making predictions at pauses and overlaps in
conversation. We find that while traditional acoustic features perform well,
part-of-speech features generally perform worse than word features. We show
that our current models outperform previously reported baselines.
| 2,018 | Computation and Language |
Counting to Explore and Generalize in Text-based Games | We propose a recurrent RL agent with an episodic exploration mechanism that
helps discovering good policies in text-based game environments. We show
promising results on a set of generated text-based games of varying difficulty
where the goal is to collect a coin located at the end of a chain of rooms. In
contrast to previous text-based RL approaches, we observe that our agent learns
policies that generalize to unseen games of greater difficulty.
| 2,019 | Computation and Language |
Joint Learning of Domain Classification and Out-of-Domain Detection with
Dynamic Class Weighting for Satisficing False Acceptance Rates | In domain classification for spoken dialog systems, correct detection of
out-of-domain (OOD) utterances is crucial because it reduces confusion and
unnecessary interaction costs between users and the systems. Previous work
usually utilizes OOD detectors that are trained separately from in-domain (IND)
classifiers, and confidence thresholding for OOD detection given target
evaluation scores. In this paper, we introduce a neural joint learning model
for domain classification and OOD detection, where dynamic class weighting is
used during the model training to satisfice a given OOD false acceptance rate
(FAR) while maximizing the domain classification accuracy. Evaluating on two
domain classification tasks for the utterances from a large spoken dialogue
system, we show that our approach significantly improves the domain
classification performance with satisficing given target FARs.
| 2,018 | Computation and Language |
Generating Titles for Web Tables | Descriptive titles provide crucial context for interpreting tables that are
extracted from web pages and are a key component of table-based web
applications. Prior approaches have attempted to produce titles by selecting
existing text snippets associated with the table. These approaches, however,
are limited by their dependence on suitable titles existing a priori. In our
user study, we observe that the relevant information for the title tends to be
scattered across the page, and often--more than 80% of the time--does not
appear verbatim anywhere in the page. We propose instead the application of a
sequence-to-sequence neural network model as a more generalizable means of
generating high-quality titles. This is accomplished by extracting many text
snippets that have potentially relevant information to the table, encoding them
into an input sequence, and using both copy and generation mechanisms in the
decoder to balance relevance and readability of the generated title. We
validate this approach with human evaluation on sample web tables and report
that while sequence models with only a copy mechanism or only a generation
mechanism are easily outperformed by simple selection-based baselines, the
model with both capabilities outperforms them all, approaching the quality of
crowdsourced titles while training on fewer than ten thousand examples. To the
best of our knowledge, the proposed technique is the first to consider text
generation methods for table titles and establishes a new state of the art.
| 2,019 | Computation and Language |
The Historical Significance of Textual Distances | Measuring similarity is a basic task in information retrieval, and now often
a building-block for more complex arguments about cultural change. But do
measures of textual similarity and distance really correspond to evidence about
cultural proximity and differentiation? To explore that question empirically,
this paper compares textual and social measures of the similarities between
genres of English-language fiction. Existing measures of textual similarity
(cosine similarity on tf-idf vectors or topic vectors) are also compared to new
strategies that use supervised learning to anchor textual measurement in a
social context.
| 2,018 | Computation and Language |
A Shared Attention Mechanism for Interpretation of Neural Automatic
Post-Editing Systems | Automatic post-editing (APE) systems aim to correct the systematic errors
made by machine translators. In this paper, we propose a neural APE system that
encodes the source (src) and machine translated (mt) sentences with two
separate encoders, but leverages a shared attention mechanism to better
understand how the two inputs contribute to the generation of the post-edited
(pe) sentences. Our empirical observations have showed that when the mt is
incorrect, the attention shifts weight toward tokens in the src sentence to
properly edit the incorrect translation. The model has been trained and
evaluated on the official data from the WMT16 and WMT17 APE IT domain
English-German shared tasks. Additionally, we have used the extra 500K
artificial data provided by the shared task. Our system has been able to
reproduce the accuracies of systems trained with the same data, while at the
same time providing better interpretability.
| 2,018 | Computation and Language |
An Efficient Approach to Encoding Context for Spoken Language
Understanding | In task-oriented dialogue systems, spoken language understanding, or SLU,
refers to the task of parsing natural language user utterances into semantic
frames. Making use of context from prior dialogue history holds the key to more
effective SLU. State of the art approaches to SLU use memory networks to encode
context by processing multiple utterances from the dialogue at each turn,
resulting in significant trade-offs between accuracy and computational
efficiency. On the other hand, downstream components like the dialogue state
tracker (DST) already keep track of the dialogue state, which can serve as a
summary of the dialogue history. In this work, we propose an efficient approach
to encoding context from prior utterances for SLU. More specifically, our
architecture includes a separate recurrent neural network (RNN) based encoding
module that accumulates dialogue context to guide the frame parsing sub-tasks
and can be shared between SLU and DST. In our experiments, we demonstrate the
effectiveness of our approach on dialogues from two domains.
| 2,018 | Computation and Language |
Lost in Translation: Analysis of Information Loss During Machine
Translation Between Polysynthetic and Fusional Languages | Machine translation from polysynthetic to fusional languages is a challenging
task, which gets further complicated by the limited amount of parallel text
available. Thus, translation performance is far from the state of the art for
high-resource and more intensively studied language pairs. To shed light on the
phenomena which hamper automatic translation to and from polysynthetic
languages, we study translations from three low-resource, polysynthetic
languages (Nahuatl, Wixarika and Yorem Nokki) into Spanish and vice versa.
Doing so, we find that in a morpheme-to-morpheme alignment an important amount
of information contained in polysynthetic morphemes has no Spanish counterpart,
and its translation is often omitted. We further conduct a qualitative analysis
and, thus, identify morpheme types that are commonly hard to align or ignored
in the translation process.
| 2,018 | Computation and Language |
Modeling, comprehending and summarizing textual content by graphs | Automatic Text Summarization strategies have been successfully employed to
digest text collections and extract its essential content. Usually, summaries
are generated using textual corpora that belongs to the same domain area where
the summary will be used. Nonetheless, there are special cases where it is not
found enough textual sources, and one possible alternative is to generate a
summary from a different domain. One manner to summarize texts consists of
using a graph model. This model allows giving more importance to words
corresponding to the main concepts from the target domain found in the
summarized text. This gives the reader an overview of the main text concepts as
well as their relationships. However, this kind of summarization presents a
significant number of repeated terms when compared to human-generated
summaries. In this paper, we present an approach to produce graph-model
extractive summaries of texts, meeting the target domain exigences and treating
the terms repetition problem. To evaluate the proposition, we performed a
series of experiments showing that the proposed approach statistically improves
the performance of a model based on Graph Centrality, achieving better
coverage, accuracy, and recall.
| 2,018 | Computation and Language |
A Simple but Effective Classification Model for Grammatical Error
Correction | We treat grammatical error correction (GEC) as a classification problem in
this study, where for different types of errors, a target word is identified,
and the classifier predicts the correct word form from a set of possible
choices. We propose a novel neural network based feature representation and
classification model, trained using large text corpora without human
annotations. Specifically we use RNNs with attention to represent both the left
and right context of a target word. All feature embeddings are learned jointly
in an end-to-end fashion. Experimental results show that our novel approach
outperforms other classifier methods on the CoNLL-2014 test set (F0.5 45.05%).
Our model is simple but effective, and is suitable for industrial production.
| 2,018 | Computation and Language |
Punctuation Prediction Model for Conversational Speech | An ASR system usually does not predict any punctuation or capitalization.
Lack of punctuation causes problems in result presentation and confuses both
the human reader andoff-the-shelf natural language processing algorithms. To
overcome these limitations, we train two variants of Deep Neural Network (DNN)
sequence labelling models - a Bidirectional Long Short-Term Memory (BLSTM) and
a Convolutional Neural Network (CNN), to predict the punctuation. The models
are trained on the Fisher corpus which includes punctuation annotation. In our
experiments, we combine time-aligned and punctuated Fisher corpus transcripts
using a sequence alignment algorithm. The neural networks are trained on Common
Web Crawl GloVe embedding of the words in Fisher transcripts aligned with
conversation side indicators and word time infomation. The CNNs yield a better
precision and BLSTMs tend to have better recall. While BLSTMs make fewer
mistakes overall, the punctuation predicted by the CNN is more accurate -
especially in the case of question marks. Our results constitute significant
evidence that the distribution of words in time, as well as pre-trained
embeddings, can be useful in the punctuation prediction task.
| 2,018 | Computation and Language |
The Interplay between Lexical Resources and Natural Language Processing | Incorporating linguistic, world and common sense knowledge into AI/NLP
systems is currently an important research area, with several open problems and
challenges. At the same time, processing and storing this knowledge in lexical
resources is not a straightforward task. This tutorial proposes to address
these complementary goals from two methodological perspectives: the use of NLP
methods to help the process of constructing and enriching lexical resources and
the use of lexical resources for improving NLP applications. Two main types of
audience can benefit from this tutorial: those working on language resources
who are interested in becoming acquainted with automatic NLP techniques, with
the end goal of speeding and/or easing up the process of resource curation; and
on the other hand, researchers in NLP who would like to benefit from the
knowledge of lexical resources to improve their systems and models. The slides
of the tutorial are available at https://bitbucket.org/luisespinosa/lr-nlp/
| 2,018 | Computation and Language |
A Neural Approach to Language Variety Translation | In this paper we present the first neural-based machine translation system
trained to translate between standard national varieties of the same language.
We take the pair Brazilian - European Portuguese as an example and compare the
performance of this method to a phrase-based statistical machine translation
system. We report a performance improvement of 0.9 BLEU points in translating
from European to Brazilian Portuguese and 0.2 BLEU points when translating in
the opposite direction. We also carried out a human evaluation experiment with
native speakers of Brazilian Portuguese which indicates that humans prefer the
output produced by the neural-based system in comparison to the statistical
system.
| 2,018 | Computation and Language |
Transparent, Efficient, and Robust Word Embedding Access with WOMBAT | We present WOMBAT, a Python tool which supports NLP practitioners in
accessing word embeddings from code. WOMBAT addresses common research problems,
including unified access, scaling, and robust and reproducible preprocessing.
Code that uses WOMBAT for accessing word embeddings is not only cleaner, more
readable, and easier to reuse, but also much more efficient than code using
standard in-memory methods: a Python script using WOMBAT for evaluating seven
large word embedding collections (8.7M embedding vectors in total) on a simple
SemEval sentence similarity task involving 250 raw sentence pairs completes in
under ten seconds end-to-end on a standard notebook computer.
| 2,018 | Computation and Language |
hep-th | We apply techniques in natural language processing, computational
linguistics, and machine-learning to investigate papers in hep-th and four
related sections of the arXiv: hep-ph, hep-lat, gr-qc, and math-ph. All of the
titles of papers in each of these sections, from the inception of the arXiv
until the end of 2017, are extracted and treated as a corpus which we use to
train the neural network Word2Vec. A comparative study of common n-grams,
linear syntactical identities, word cloud and word similarities is carried out.
We find notable scientific and sociological differences between the fields. In
conjunction with support vector machines, we also show that the syntactic
structure of the titles in different sub-fields of high energy and mathematical
physics are sufficiently different that a neural network can perform a binary
classification of formal versus phenomenological sections with 87.1% accuracy,
and can perform a finer five-fold classification across all sections with 65.1%
accuracy.
| 2,018 | Computation and Language |
Representation Mapping: A Novel Approach to Generate High-Quality
Multi-Lingual Emotion Lexicons | In the past years, sentiment analysis has increasingly shifted attention to
representational frameworks more expressive than semantic polarity (being
positive, negative or neutral). However, these richer formats (like Basic
Emotions or Valence-Arousal-Dominance, and variants therefrom), rooted in
psychological research, tend to proliferate the number of representation
schemes for emotion encoding. Thus, a large amount of representationally
incompatible emotion lexicons has been developed by various research groups
adopting one or the other emotion representation format. As a consequence, the
reusability of these resources decreases as does the comparability of systems
using them. In this paper, we propose to solve this dilemma by methods and
tools which map different representation formats onto each other for the sake
of mutual compatibility and interoperability of language resources. We present
the first large-scale investigation of such representation mappings for four
typologically diverse languages and find evidence that our approach produces
(near-)gold quality emotion lexicons, even in cross-lingual settings. Finally,
we use our models to create new lexicons for eight typologically diverse
languages.
| 2,018 | Computation and Language |
Pragmatic approach to structured data querying via natural language
interface | As the use of technology increases and data analysis becomes integral in many
businesses, the ability to quickly access and interpret data has become more
important than ever. Information retrieval technologies are being utilized by
organizations and companies to manage their information systems and processes.
Despite information retrieval of a large amount of data being efficient
organized in relational databases, a user still needs to master the DB
language/schema to completely formulate the queries. This puts a burden on
organizations and companies to hire employees that are proficient in DB
languages/schemas to formulate queries. To reduce some of the burden on already
overstretched data teams, many organizations are looking for tools that allow
non-developers to query their databases. Unfortunately, writing a valid SQL
query that answers the question a user is trying to ask isn't always easy. Even
seemingly simple questions, like "Which start-up companies received more than
$200M in funding?" can actually be very hard to answer, let alone convert into
a SQL query. How do you define start-up companies? By size, location, duration
of time they have been incorporated? This may be fine if a user is working with
a database they're already familiar with, but what if users are not familiar
with the database. What is needed is a centralized system that can effectively
translate natural language queries into specific database queries for different
customer database types. There is a number of factors that can dramatically
affect the system architecture and the set of algorithms used to translate NL
queries into a structured query representation.
| 2,018 | Computation and Language |
Improving part-of-speech tagging via multi-task learning and
character-level word representations | In this paper, we explore the ways to improve POS-tagging using various types
of auxiliary losses and different word representations. As a baseline, we
utilized a BiLSTM tagger, which is able to achieve state-of-the-art results on
the sequence labelling tasks. We developed a new method for character-level
word representation using feedforward neural network. Such representation gave
us better results in terms of speed and performance of the model. We also
applied a novel technique of pretraining such word representations with
existing word vectors. Finally, we designed a new variant of auxiliary loss for
sequence labelling tasks: an additional prediction of the neighbour labels.
Such loss forces a model to learn the dependencies in-side a sequence of labels
and accelerates the process of training. We test these methods on English and
Russian languages.
| 2,018 | Computation and Language |
Modeling Language Variation and Universals: A Survey on Typological
Linguistics for Natural Language Processing | Linguistic typology aims to capture structural and semantic variation across
the world's languages. A large-scale typology could provide excellent guidance
for multilingual Natural Language Processing (NLP), particularly for languages
that suffer from the lack of human labeled resources. We present an extensive
literature survey on the use of typological information in the development of
NLP techniques. Our survey demonstrates that to date, the use of information in
existing typological databases has resulted in consistent but modest
improvements in system performance. We show that this is due to both intrinsic
limitations of databases (in terms of coverage and feature granularity) and
under-employment of the typological features included in them. We advocate for
a new approach that adapts the broad and discrete nature of typological
categories to the contextual and continuous nature of machine learning
algorithms used in contemporary NLP. In particular, we suggest that such
approach could be facilitated by recent developments in data-driven induction
of typological knowledge.
| 2,020 | Computation and Language |
Neural Random Projections for Language Modelling | Neural network-based language models deal with data sparsity problems by
mapping the large discrete space of words into a smaller continuous space of
real-valued vectors. By learning distributed vector representations for words,
each training sample informs the neural network model about a combinatorial
number of other patterns. In this paper, we exploit the sparsity in natural
language even further by encoding each unique input word using a fixed sparse
random representation. These sparse codes are then projected onto a smaller
embedding space which allows for the encoding of word occurrences from a
possibly unknown vocabulary, along with the creation of more compact language
models using a reduced number of parameters. We investigate the properties of
our encoding mechanism empirically, by evaluating its performance on the widely
used Penn Treebank corpus. We show that guaranteeing approximately equidistant
(nearly orthogonal) vector representations for unique discrete inputs is enough
to provide the neural network model with enough information to learn --and make
use-- of distributed representations for these inputs.
| 2,018 | Computation and Language |
Topic Discovery in Massive Text Corpora Based on Min-Hashing | The task of discovering topics in text corpora has been dominated by Latent
Dirichlet Allocation and other Topic Models for over a decade. In order to
apply these approaches to massive text corpora, the vocabulary needs to be
reduced considerably and large computer clusters and/or GPUs are typically
required. Moreover, the number of topics must be provided beforehand but this
depends on the corpus characteristics and it is often difficult to estimate,
especially for massive text corpora. Unfortunately, both topic quality and time
complexity are sensitive to this choice. This paper describes an alternative
approach to discover topics based on Min-Hashing, which can handle massive text
corpora and large vocabularies using modest computer hardware and does not
require to fix the number of topics in advance. The basic idea is to generate
multiple random partitions of the corpus vocabulary to find sets of highly
co-occurring words, which are then clustered to produce the final topics. In
contrast to probabilistic topic models where topics are distributions over the
complete vocabulary, the topics discovered by the proposed approach are sets of
highly co-occurring words. Interestingly, these topics underlie various
thematics with different levels of granularity. An extensive qualitative and
quantitative evaluation using the 20 Newsgroups (18K), Reuters (800K), Spanish
Wikipedia (1M), and English Wikipedia (5M) corpora shows that the proposed
approach is able to consistently discover meaningful and coherent topics.
Remarkably, the time complexity of the proposed approach is linear with respect
to corpus and vocabulary size; a non-parallel implementation was able to
discover topics from the entire English edition of Wikipedia with over 5
million documents and 1 million words in less than 7 hours.
| 2,019 | Computation and Language |
Improved training of neural trans-dimensional random field language
models with dynamic noise-contrastive estimation | A new whole-sentence language model - neural trans-dimensional random field
language model (neural TRF LM), where sentences are modeled as a collection of
random fields, and the potential function is defined by a neural network, has
been introduced and successfully trained by noise-contrastive estimation (NCE).
In this paper, we extend NCE and propose dynamic noise-contrastive estimation
(DNCE) to solve the two problems observed in NCE training. First, a dynamic
noise distribution is introduced and trained simultaneously to converge to the
data distribution. This helps to significantly cut down the noise sample number
used in NCE and reduce the training cost. Second, DNCE discriminates between
sentences generated from the noise distribution and sentences generated from
the interpolation of the data distribution and the noise distribution. This
alleviates the overfitting problem caused by the sparseness of the training
set. With DNCE, we can successfully and efficiently train neural TRF LMs on
large corpus (about 0.8 billion words) with large vocabulary (about 568 K
words). Neural TRF LMs perform as good as LSTM LMs with less parameters and
being 5x~114x faster in rescoring sentences. Interpolating neural TRF LMs with
LSTM LMs and n-gram LMs can further reduce the error rates.
| 2,018 | Computation and Language |
Reaching Human-level Performance in Automatic Grammatical Error
Correction: An Empirical Study | Neural sequence-to-sequence (seq2seq) approaches have proven to be successful
in grammatical error correction (GEC). Based on the seq2seq framework, we
propose a novel fluency boost learning and inference mechanism. Fluency
boosting learning generates diverse error-corrected sentence pairs during
training, enabling the error correction model to learn how to improve a
sentence's fluency from more instances, while fluency boosting inference allows
the model to correct a sentence incrementally with multiple inference steps.
Combining fluency boost learning and inference with convolutional seq2seq
models, our approach achieves the state-of-the-art performance: 75.72 (F_{0.5})
on CoNLL-2014 10 annotation dataset and 62.42 (GLEU) on JFLEG test set
respectively, becoming the first GEC system that reaches human-level
performance (72.58 for CoNLL and 62.37 for JFLEG) on both of the benchmarks.
| 2,018 | Computation and Language |
Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org
Annotations | Goal-oriented dialogue systems typically communicate with a backend (e.g.
database, Web API) to complete certain tasks to reach a goal. The intents that
a dialogue system can recognize are mostly included to the system by the
developer statically. For an open dialogue system that can work on more than a
small set of well curated data and APIs, this manual intent creation will not
scalable. In this paper, we introduce a straightforward methodology for intent
creation based on semantic annotation of data and services on the web. With
this method, the Natural Language Understanding (NLU) module of a goal-oriented
dialogue system can adapt to newly introduced APIs without requiring heavy
developer involvement. We were able to extract intents and necessary slots to
be filled from schema.org annotations. We were also able to create a set of
initial training sentences for classifying user utterances into the generated
intents. We demonstrate our approach on the NLU module of a state-of-the art
dialogue system development framework.
| 2,018 | Computation and Language |
Patient representation learning and interpretable evaluation using
clinical notes | We have three contributions in this work: 1. We explore the utility of a
stacked denoising autoencoder and a paragraph vector model to learn
task-independent dense patient representations directly from clinical notes. To
analyze if these representations are transferable across tasks, we evaluate
them in multiple supervised setups to predict patient mortality, primary
diagnostic and procedural category, and gender. We compare their performance
with sparse representations obtained from a bag-of-words model. We observe that
the learned generalized representations significantly outperform the sparse
representations when we have few positive instances to learn from, and there is
an absence of strong lexical features. 2. We compare the model performance of
the feature set constructed from a bag of words to that obtained from medical
concepts. In the latter case, concepts represent problems, treatments, and
tests. We find that concept identification does not improve the classification
performance. 3. We propose novel techniques to facilitate model
interpretability. To understand and interpret the representations, we explore
the best encoded features within the patient representations obtained from the
autoencoder model. Further, we calculate feature sensitivity across two
networks to identify the most significant input features for different
classification tasks when we use these pretrained representations as the
supervised input. We successfully extract the most influential features for the
pipeline using this technique.
| 2,018 | Computation and Language |
Simpler but More Accurate Semantic Dependency Parsing | While syntactic dependency annotations concentrate on the surface or
functional structure of a sentence, semantic dependency annotations aim to
capture between-word relationships that are more closely related to the meaning
of a sentence, using graph-structured representations. We extend the LSTM-based
syntactic parser of Dozat and Manning (2017) to train on and generate these
graph structures. The resulting system on its own achieves state-of-the-art
performance, beating the previous, substantially more complex state-of-the-art
system by 0.6% labeled F1. Adding linguistically richer input representations
pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.
| 2,018 | Computation and Language |
Polarity and Intensity: the Two Aspects of Sentiment Analysis | Current multimodal sentiment analysis frames sentiment score prediction as a
general Machine Learning task. However, what the sentiment score actually
represents has often been overlooked. As a measurement of opinions and
affective states, a sentiment score generally consists of two aspects: polarity
and intensity. We decompose sentiment scores into these two aspects and study
how they are conveyed through individual modalities and combined multimodal
models in a naturalistic monologue setting. In particular, we build unimodal
and multimodal multi-task learning models with sentiment score prediction as
the main task and polarity and/or intensity classification as the auxiliary
tasks. Our experiments show that sentiment analysis benefits from multi-task
learning, and individual modalities differ when conveying the polarity and
intensity aspects of sentiment.
| 2,018 | Computation and Language |
Sequence-to-Sequence Data Augmentation for Dialogue Language
Understanding | In this paper, we study the problem of data augmentation for language
understanding in task-oriented dialogue system. In contrast to previous work
which augments an utterance without considering its relation with other
utterances, we propose a sequence-to-sequence generation based data
augmentation framework that leverages one utterance's same semantic
alternatives in the training data. A novel diversity rank is incorporated into
the utterance representation to make the model produce diverse utterances and
these diversely augmented utterances help to improve the language understanding
module. Experimental results on the Airline Travel Information System dataset
and a newly created semantic frame annotation on Stanford Multi-turn,
Multidomain Dialogue Dataset show that our framework achieves significant
improvements of 6.38 and 10.04 F-scores respectively when only a training set
of hundreds utterances is represented. Case studies also confirm that our
method generates diverse utterances.
| 2,018 | Computation and Language |
Encoding Spatial Relations from Natural Language | Natural language processing has made significant inroads into learning the
semantics of words through distributional approaches, however representations
learnt via these methods fail to capture certain kinds of information implicit
in the real world. In particular, spatial relations are encoded in a way that
is inconsistent with human spatial reasoning and lacking invariance to
viewpoint changes. We present a system capable of capturing the semantics of
spatial relations such as behind, left of, etc from natural language. Our key
contributions are a novel multi-modal objective based on generating images of
scenes from their textual descriptions, and a new dataset on which to train it.
We demonstrate that internal representations are robust to meaning preserving
transformations of descriptions (paraphrase invariance), while viewpoint
invariance is an emergent property of the system.
| 2,018 | Computation and Language |
Towards Automation of Sense-type Identification of Verbs in
OntoSenseNet(Telugu) | In this paper, we discuss the enrichment of a manually developed resource of
Telugu lexicon, OntoSenseNet. OntoSenseNet is a ontological sense annotated
lexicon that marks each verb of Telugu with a primary and a secondary sense.
The area of research is relatively recent but has a large scope of development.
We provide an introductory work to enrich the OntoSenseNet to promote further
research in Telugu. Classifiers are adopted to learn the sense relevant
features of the words in the resource and also to automate the tagging of
sense-types for verbs. We perform a comparative analysis of different
classifiers applied on OntoSenseNet. The results of the experiment prove that
automated enrichment of the resource is effective using SVM classifiers and
Adaboost ensemble.
| 2,018 | Computation and Language |
BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using
Word-level Annotations | The presented work aims at generating a systematically annotated corpus that
can support the enhancement of sentiment analysis tasks in Telugu using
word-level sentiment annotations. From OntoSenseNet, we extracted 11,000
adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by
language experts. We discuss the methodology followed for the polarity
annotations and validate the developed resource. This work aims at developing a
benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a
model where lexeme annotations are applied for sentiment predictions. The
fundamental aim of this paper is to validate and study the possibility of
utilizing machine learning algorithms, word-level sentiment annotations in the
task of automated sentiment identification. Furthermore, accuracy is improved
by annotating the bi-grams extracted from the target corpus.
| 2,018 | Computation and Language |
Generating Mandarin and Cantonese F0 Contours with Decision Trees and
BLSTMs | This paper models the fundamental frequency contours on both Mandarin and
Cantonese speech with decision trees and DNNs (deep neural networks). Different
kinds of f0 representations and model architectures are tested for decision
trees and DNNs. A new model called Additive-BLSTM (additive bidirectional long
short term memory) that predicts a base f0 contour and a residual f0 contour
with two BLSTMs is proposed. With respect to objective measures of RMSE and
correlation, applying tone-dependent trees together with sample normalization
and delta feature regularization within decision tree framework performs best.
While the new Additive-BLSTM model with delta feature regularization performs
even better. Subjective listening tests on both Mandarin and Cantonese
comparing Random Forest model (multiple decision trees) and the Additive-BLSTM
model were also held and confirmed the advantage of the new model according to
the listeners' preference.
| 2,018 | Computation and Language |
A Convolutional Neural Network for Aspect Sentiment Classification | With the development of the Internet, natural language processing (NLP), in
which sentiment analysis is an important task, became vital in information
processing.Sentiment analysis includes aspect sentiment classification. Aspect
sentiment can provide complete and in-depth results with increased attention on
aspect-level. Different context words in a sentence influence the sentiment
polarity of a sentence variably, and polarity varies based on the different
aspects in a sentence. Take the sentence, 'I bought a new camera. The picture
quality is amazing but the battery life is too short.'as an example. If the
aspect is picture quality, then the expected sentiment polarity is 'positive',
if the battery life aspect is considered, then the sentiment polarity should be
'negative'; therefore, aspect is important to consider when we explore aspect
sentiment in the sentence. Recurrent neural network (RNN) is regarded as a good
model to deal with natural language processing, and RNNs has get good
performance on aspect sentiment classification including Target-Dependent LSTM
(TD-LSTM) ,Target-Connection LSTM (TC-LSTM) (Tang, 2015a, b), AE-LSTM, AT-LSTM,
AEAT-LSTM (Wang et al., 2016).There are also extensive literatures on sentiment
classification utilizing convolutional neural network, but there is little
literature on aspect sentiment classification using convolutional neural
network. In our paper, we develop attention-based input layers in which aspect
information is considered by input layer. We then incorporate attention-based
input layers into convolutional neural network (CNN) to introduce context words
information. In our experiment, incorporating aspect information into CNN
improves the latter's aspect sentiment classification performance without using
syntactic parser or external sentiment lexicons in a benchmark dataset from
Twitter but get better performance compared with other models.
| 2,018 | Computation and Language |
Global Transition-based Non-projective Dependency Parsing | Shi, Huang, and Lee (2017) obtained state-of-the-art results for English and
Chinese dependency parsing by combining dynamic-programming implementations of
transition-based dependency parsers with a minimal set of bidirectional LSTM
features. However, their results were limited to projective parsing. In this
paper, we extend their approach to support non-projectivity by providing the
first practical implementation of the MH_4 algorithm, an $O(n^4)$ mildly
nonprojective dynamic-programming parser with very high coverage on
non-projective treebanks. To make MH_4 compatible with minimal transition-based
feature sets, we introduce a transition-based interpretation of it in which
parser items are mapped to sequences of transitions. We thus obtain the first
implementation of global decoding for non-projective transition-based parsing,
and demonstrate empirically that it is more effective than its projective
counterpart in parsing a number of highly non-projective languages
| 2,018 | Computation and Language |
Seq2RDF: An end-to-end application for deriving Triples from Natural
Language Text | We present an end-to-end approach that takes unstructured textual input and
generates structured output compliant with a given vocabulary. Inspired by
recent successes in neural machine translation, we treat the triples within a
given knowledge graph as an independent graph language and propose an
encoder-decoder framework with an attention mechanism that leverages knowledge
graph embeddings. Our model learns the mapping from natural language text to
triple representation in the form of subject-predicate-object using the
selected knowledge graph vocabulary. Experiments on three different data sets
show that we achieve competitive F1-Measures over the baselines using our
simple yet effective approach. A demo video is included.
| 2,018 | Computation and Language |
Zipf's law in 50 languages: its structural pattern, linguistic
interpretation, and cognitive motivation | Zipf's law has been found in many human-related fields, including language,
where the frequency of a word is persistently found as a power law function of
its frequency rank, known as Zipf's law. However, there is much dispute whether
it is a universal law or a statistical artifact, and little is known about what
mechanisms may have shaped it. To answer these questions, this study conducted
a large scale cross language investigation into Zipf's law. The statistical
results show that Zipf's laws in 50 languages all share a 3-segment structural
pattern, with each segment demonstrating distinctive linguistic properties and
the lower segment invariably bending downwards to deviate from theoretical
expectation. This finding indicates that this deviation is a fundamental and
universal feature of word frequency distributions in natural languages, not the
statistical error of low frequency words. A computer simulation based on the
dual-process theory yields Zipf's law with the same structural pattern,
suggesting that Zipf's law of natural languages are motivated by common
cognitive mechanisms. These results show that Zipf's law in languages is
motivated by cognitive mechanisms like dual-processing that govern human verbal
behaviors.
| 2,018 | Computation and Language |
Chinese Lexical Analysis with Deep Bi-GRU-CRF Network | Lexical analysis is believed to be a crucial step towards natural language
understanding and has been widely studied. Recent years, end-to-end lexical
analysis models with recurrent neural networks have gained increasing
attention. In this report, we introduce a deep Bi-GRU-CRF network that jointly
models word segmentation, part-of-speech tagging and named entity recognition
tasks. We trained the model using several massive corpus pre-tagged by our best
Chinese lexical analysis tool, together with a small, yet high-quality human
annotated corpus. We conducted balanced sampling between different corpora to
guarantee the influence of human annotations, and fine-tune the CRF decoding
layer regularly during the training progress. As evaluated by linguistic
experts, the model achieved a 95.5% accuracy on the test set, roughly 13%
relative error reduction over our (previously) best Chinese lexical analysis
tool. The model is computationally efficient, achieving the speed of 2.3K
characters per second with one thread.
| 2,018 | Computation and Language |
Neural Language Codes for Multilingual Acoustic Models | Multilingual Speech Recognition is one of the most costly AI problems,
because each language (7,000+) and even different accents require their own
acoustic models to obtain best recognition performance. Even though they all
use the same phoneme symbols, each language and accent imposes its own coloring
or "twang". Many adaptive approaches have been proposed, but they require
further training, additional data and generally are inferior to monolingually
trained models. In this paper, we propose a different approach that uses a
large multilingual model that is \emph{modulated} by the codes generated by an
ancillary network that learns to code useful differences between the "twangs"
or human language.
We use Meta-Pi networks to have one network (the language code net) gate the
activity of neurons in another (the acoustic model nets). Our results show that
during recognition multilingual Meta-Pi networks quickly adapt to the proper
language coloring without retraining or new data, and perform better than
monolingually trained networks. The model was evaluated by training acoustic
modeling nets and modulating language code nets jointly and optimize them for
best recognition performance.
| 2,018 | Computation and Language |
A Formal Ontology-Based Classification of Lexemes and its Applications | The paper describes the enrichment of OntoSenseNet - a verb-centric lexical
resource for Indian Languages. A major contribution of this work is
preservation of an authentic Telugu dictionary by developing a computational
version of the same. It is important because native speakers can better
annotate the sense-types when both the word and its meaning are in Telugu.
Hence efforts are made to develop the aforementioned Telugu dictionary and
annotations are done manually. The manually annotated gold standard corpus
consists 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by
native speakers according to defined annotation guidelines. In this paper, we
provide an overview of the annotation procedure and present the validation of
the developed resource through inter-annotator agreement. Additional words from
Telugu WordNet are added to our resource and are crowd-sourced for annotation.
The statistics are compared with the sense-annotated lexicon, our resource for
more insights.
| 2,018 | Computation and Language |
Natural Language Processing for Music Knowledge Discovery | Today, a massive amount of musical knowledge is stored in written form, with
testimonies dated as far back as several centuries ago. In this work, we
present different Natural Language Processing (NLP) approaches to harness the
potential of these text collections for automatic music knowledge discovery,
covering different phases in a prototypical NLP pipeline, namely corpus
compilation, text-mining, information extraction, knowledge graph generation
and sentiment analysis. Each of these approaches is presented alongside
different use cases (i.e., flamenco, Renaissance and popular music) where large
collections of documents are processed, and conclusions stemming from
data-driven analyses are presented and discussed.
| 2,018 | Computation and Language |
The price of debiasing automatic metrics in natural language evaluation | For evaluating generation systems, automatic metrics such as BLEU cost
nothing to run but have been shown to correlate poorly with human judgment,
leading to systematic bias against certain model improvements. On the other
hand, averaging human judgments, the unbiased gold standard, is often too
expensive. In this paper, we use control variates to combine automatic metrics
with human evaluation to obtain an unbiased estimator with lower cost than
human evaluation alone. In practice, however, we obtain only a 7-13% cost
reduction on evaluating summarization and open-response question answering
systems. We then prove that our estimator is optimal: there is no unbiased
estimator with lower cost. Our theory further highlights the two fundamental
bottlenecks---the automatic metric and the prompt shown to human
evaluators---both of which need to be improved to obtain greater cost savings.
| 2,018 | Computation and Language |
The Data Science of Hollywood: Using Emotional Arcs of Movies to Drive
Business Model Innovation in Entertainment Industries | Much of business literature addresses the issues of consumer-centric design:
how can businesses design customized services and products which accurately
reflect consumer preferences? This paper uses data science natural language
processing methodology to explore whether and to what extent emotions shape
consumer preferences for media and entertainment content. Using a unique
filtered dataset of 6,174 movie scripts, we generate a mapping of screen
content to capture the emotional trajectory of each motion picture. We then
combine the obtained mappings into clusters which represent groupings of
consumer emotional journeys. These clusters are used to predict overall success
parameters of the movies including box office revenues, viewer satisfaction
levels (captured by IMDb ratings), awards, as well as the number of viewers'
and critics' reviews. We find that like books all movie stories are dominated
by 6 basic shapes. The highest box offices are associated with the Man in a
Hole shape which is characterized by an emotional fall followed by an emotional
rise. This shape results in financially successful movies irrespective of genre
and production budget. Yet, Man in a Hole succeeds not because it produces most
"liked" movies but because it generates most "talked about" movies.
Interestingly, a carefully chosen combination of production budget and genre
may produce a financially successful movie with any emotional shape.
Implications of this analysis for generating on-demand content and for driving
business model innovation in entertainment industries are discussed.
| 2,020 | Computation and Language |
A Concept Specification and Abstraction-based Semantic Representation:
Addressing the Barriers to Rule-based Machine Translation | Rule-based machine translation is more data efficient than the big data-based
machine translation approaches, making it appropriate for languages with low
bilingual corpus resources -- i.e., minority languages. However, the rule-based
approach has declined in popularity relative to its big data cousins primarily
because of the extensive training and labour required to define the language
rules. To address this, we present a semantic representation that 1) treats all
bits of meaning as individual concepts that 2) modify or further specify one
another to build a network that relates entities in space and time. Also, the
representation can 3) encapsulate propositions and thereby define concepts in
terms of other concepts, supporting the abstraction of underlying linguistic
and ontological details. These features afford an exact, yet intuitive semantic
representation aimed at handling the great variety in language and reducing
labour and training time. The proposed natural language generation, parsing,
and translation strategies are also amenable to probabilistic modeling and thus
to learning the necessary rules from example data.
| 2,019 | Computation and Language |
Sliced Recurrent Neural Networks | Recurrent neural networks have achieved great success in many NLP tasks.
However, they have difficulty in parallelization because of the recurrent
structure, so it takes much time to train RNNs. In this paper, we introduce
sliced recurrent neural networks (SRNNs), which could be parallelized by
slicing the sequences into many subsequences. SRNNs have the ability to obtain
high-level information through multiple layers with few extra parameters. We
prove that the standard RNN is a special case of the SRNN when we use linear
activation functions. Without changing the recurrent units, SRNNs are 136 times
as fast as standard RNNs and could be even faster when we train longer
sequences. Experiments on six largescale sentiment analysis datasets show that
SRNNs achieve better performance than standard RNNs.
| 2,018 | Computation and Language |
Sequential Copying Networks | Copying mechanism shows effectiveness in sequence-to-sequence based neural
network models for text generation tasks, such as abstractive sentence
summarization and question generation. However, existing works on modeling
copying or pointing mechanism only considers single word copying from the
source sentences. In this paper, we propose a novel copying framework, named
Sequential Copying Networks (SeqCopyNet), which not only learns to copy single
words, but also copies sequences from the input sentence. It leverages the
pointer networks to explicitly select a sub-span from the source side to target
side, and integrates this sequential copying mechanism to the generation
process in the encoder-decoder paradigm. Experiments on abstractive sentence
summarization and question generation tasks show that the proposed SeqCopyNet
can copy meaningful spans and outperforms the baseline models.
| 2,018 | Computation and Language |
Neural Document Summarization by Jointly Learning to Score and Select
Sentences | Sentence scoring and sentence selection are two main steps in extractive
document summarization systems. However, previous works treat them as two
separated subtasks. In this paper, we present a novel end-to-end neural network
framework for extractive document summarization by jointly learning to score
and select sentences. It first reads the document sentences with a hierarchical
encoder to obtain the representation of sentences. Then it builds the output
summary by extracting sentences one by one. Different from previous methods,
our approach integrates the selection strategy into the scoring model, which
directly predicts the relative importance given previously selected sentences.
Experiments on the CNN/Daily Mail dataset show that the proposed framework
significantly outperforms the state-of-the-art extractive summarization models.
| 2,018 | Computation and Language |
Testing Untestable Neural Machine Translation: An Industrial Case | Neural Machine Translation (NMT) has been widely adopted recently due to its
advantages compared with the traditional Statistical Machine Translation (SMT).
However, an NMT system still often produces translation failures due to the
complexity of natural language and sophistication in designing neural networks.
While in-house black-box system testing based on reference translations (i.e.,
examples of valid translations) has been a common practice for NMT quality
assurance, an increasingly critical industrial practice, named in-vivo testing,
exposes unseen types or instances of translation failures when real users are
using a deployed industrial NMT system. To fill the gap of lacking test oracle
for in-vivo testing of an NMT system, in this paper, we propose a new approach
for automatically identifying translation failures, without requiring reference
translations for a translation task; our approach can directly serve as a test
oracle for in-vivo testing. Our approach focuses on properties of natural
language translation that can be checked systematically and uses information
from both the test inputs (i.e., the texts to be translated) and the test
outputs (i.e., the translations under inspection) of the NMT system. Our
evaluation conducted on real-world datasets shows that our approach can
effectively detect targeted property violations as translation failures. Our
experiences on deploying our approach in both production and development
environments of WeChat (a messenger app with over one billion monthly active
users) demonstrate high effectiveness of our approach along with high industry
impact.
| 2,018 | Computation and Language |
Natural Language Processing for Information Extraction | With rise of digital age, there is an explosion of information in the form of
news, articles, social media, and so on. Much of this data lies in unstructured
form and manually managing and effectively making use of it is tedious, boring
and labor intensive. This explosion of information and need for more
sophisticated and efficient information handling tools gives rise to
Information Extraction(IE) and Information Retrieval(IR) technology.
Information Extraction systems takes natural language text as input and
produces structured information specified by certain criteria, that is relevant
to a particular application. Various sub-tasks of IE such as Named Entity
Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction,
Knowledge Base reasoning forms the building blocks of various high end Natural
Language Processing (NLP) tasks such as Machine Translation, Question-Answering
System, Natural Language Understanding, Text Summarization and Digital
Assistants like Siri, Cortana and Google Now. This paper introduces Information
Extraction technology, its various sub-tasks, highlights state-of-the-art
research in various IE subtasks, current challenges and future research
directions.
| 2,018 | Computation and Language |
CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction | In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw
challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for
judgment prediction. \dataset contains more than $2.6$ million criminal cases
published by the Supreme People's Court of China, which are several times
larger than other datasets in existing works on judgment prediction. Moreover,
the annotations of judgment results are more detailed and rich. It consists of
applicable law articles, charges, and prison terms, which are expected to be
inferred according to the fact descriptions of cases. For comparison, we
implement several conventional text classification baselines for judgment
prediction and experimental results show that it is still a challenge for
current models to predict the judgment results of legal cases, especially on
prison terms. To help the researchers make improvements on legal judgment
prediction, both \dataset and baselines will be released after the CAIL
competition\footnote{http://cail.cipsc.org.cn/}.
| 2,018 | Computation and Language |
From Text to Topics in Healthcare Records: An Unsupervised Graph
Partitioning Methodology | Electronic Healthcare Records contain large volumes of unstructured data,
including extensive free text. Yet this source of detailed information often
remains under-used because of a lack of methodologies to extract interpretable
content in a timely manner. Here we apply network-theoretical tools to analyse
free text in Hospital Patient Incident reports from the National Health
Service, to find clusters of documents with similar content in an unsupervised
manner at different levels of resolution. We combine deep neural network
paragraph vector text-embedding with multiscale Markov Stability community
detection applied to a sparsified similarity graph of document vectors, and
showcase the approach on incident reports from Imperial College Healthcare NHS
Trust, London. The multiscale community structure reveals different levels of
meaning in the topics of the dataset, as shown by descriptive terms extracted
from the clusters of records. We also compare a posteriori against hand-coded
categories assigned by healthcare personnel, and show that our approach
outperforms LDA-based models. Our content clusters exhibit good correspondence
with two levels of hand-coded categories, yet they also provide further medical
detail in certain areas and reveal complementary descriptors of incidents
beyond the external classification taxonomy.
| 2,018 | Computation and Language |
Robust and Scalable Differentiable Neural Computer for Question
Answering | Deep learning models are often not easily adaptable to new tasks and require
task-specific adjustments. The differentiable neural computer (DNC), a
memory-augmented neural network, is designed as a general problem solver which
can be used in a wide range of tasks. But in reality, it is hard to apply this
model to new tasks. We analyze the DNC and identify possible improvements
within the application of question answering. This motivates a more robust and
scalable DNC (rsDNC). The objective precondition is to keep the general
character of this model intact while making its application more reliable and
speeding up its required training time. The rsDNC is distinguished by a more
robust training, a slim memory unit and a bidirectional architecture. We not
only achieve new state-of-the-art performance on the bAbI task, but also
minimize the performance variance between different initializations.
Furthermore, we demonstrate the simplified applicability of the rsDNC to new
tasks with passable results on the CNN RC task without adaptions.
| 2,018 | Computation and Language |
A Deep Generative Model of Vowel Formant Typology | What makes some types of languages more probable than others? For instance,
we know that almost all spoken languages contain the vowel phoneme /i/; why
should that be? The field of linguistic typology seeks to answer these
questions and, thereby, divine the mechanisms that underlie human language. In
our work, we tackle the problem of vowel system typology, i.e., we propose a
generative probability model of which vowels a language contains. In contrast
to previous work, we work directly with the acoustic information -- the first
two formant values -- rather than modeling discrete sets of phonemic symbols
(IPA). We develop a novel generative probability model and report results based
on a corpus of 233 languages.
| 2,018 | Computation and Language |
On the Complexity and Typology of Inflectional Morphological Systems | We quantify the linguistic complexity of different languages' morphological
systems. We verify that there is an empirical trade-off between paradigm size
and irregularity: a language's inflectional paradigms may be either large in
size or highly irregular, but never both. Our methodology measures paradigm
irregularity as the entropy of the surface realization of a paradigm -- how
hard it is to jointly predict all the surface forms of a paradigm. We estimate
this by a variational approximation. Our measurements are taken on large
morphological paradigms from 31 typologically diverse languages.
| 2,018 | Computation and Language |
Latent Semantic Analysis Approach for Document Summarization Based on
Word Embeddings | Since the amount of information on the internet is growing rapidly, it is not
easy for a user to find relevant information for his/her query. To tackle this
issue, much attention has been paid to Automatic Document Summarization. The
key point in any successful document summarizer is a good document
representation. The traditional approaches based on word overlapping mostly
fail to produce that kind of representation. Word embedding, distributed
representation of words, has shown an excellent performance that allows words
to match on semantic level. Naively concatenating word embeddings makes the
common word dominant which in turn diminish the representation quality. In this
paper, we employ word embeddings to improve the weighting schemes for
calculating the input matrix of Latent Semantic Analysis method. Two
embedding-based weighting schemes are proposed and then combined to calculate
the values of this matrix. The new weighting schemes are modified versions of
the augment weight and the entropy frequency. The new schemes combine the
strength of the traditional weighting schemes and word embedding. The proposed
approach is experimentally evaluated on three well-known English datasets, DUC
2002, DUC 2004 and Multilingual 2015 Single-document Summarization for English.
The proposed model performs comprehensively better compared to the
state-of-the-art methods, by at least 1% ROUGE points, leading to a conclusion
that it provides a better document representation and a better document summary
as a result.
| 2,019 | Computation and Language |
Predicting Concreteness and Imageability of Words Within and Across
Languages via Word Embeddings | The notions of concreteness and imageability, traditionally important in
psycholinguistics, are gaining significance in semantic-oriented natural
language processing tasks. In this paper we investigate the predictability of
these two concepts via supervised learning, using word embeddings as
explanatory variables. We perform predictions both within and across languages
by exploiting collections of cross-lingual embeddings aligned to a single
vector space. We show that the notions of concreteness and imageability are
highly predictable both within and across languages, with a moderate loss of up
to 20% in correlation when predicting across languages. We further show that
the cross-lingual transfer via word embeddings is more efficient than the
simple transfer via bilingual dictionaries.
| 2,022 | Computation and Language |
A Combined CNN and LSTM Model for Arabic Sentiment Analysis | Deep neural networks have shown good data modelling capabilities when dealing
with challenging and large datasets from a wide range of application areas.
Convolutional Neural Networks (CNNs) offer advantages in selecting good
features and Long Short-Term Memory (LSTM) networks have proven good abilities
of learning sequential data. Both approaches have been reported to provide
improved results in areas such image processing, voice recognition, language
translation and other Natural Language Processing (NLP) tasks. Sentiment
classification for short text messages from Twitter is a challenging task, and
the complexity increases for Arabic language sentiment classification tasks
because Arabic is a rich language in morphology. In addition, the availability
of accurate pre-processing tools for Arabic is another current limitation,
along with limited research available in this area. In this paper, we
investigate the benefits of integrating CNNs and LSTMs and report obtained
improved accuracy for Arabic sentiment analysis on different datasets.
Additionally, we seek to consider the morphological diversity of particular
Arabic words by using different sentiment classification levels.
| 2,018 | Computation and Language |
Universal Word Segmentation: Implementation and Interpretation | Word segmentation is a low-level NLP task that is non-trivial for a
considerable number of languages. In this paper, we present a sequence tagging
framework and apply it to word segmentation for a wide range of languages with
different writing systems and typological characteristics. Additionally, we
investigate the correlations between various typological factors and word
segmentation accuracy. The experimental results indicate that segmentation
accuracy is positively related to word boundary markers and negatively to the
number of unique non-segmental terms. Based on the analysis, we design a small
set of language-specific settings and extensively evaluate the segmentation
system on the Universal Dependencies datasets. Our model obtains
state-of-the-art accuracies on all the UD languages. It performs substantially
better on languages that are non-trivial to segment, such as Chinese, Japanese,
Arabic and Hebrew, when compared to previous work.
| 2,018 | Computation and Language |
Towards Enhancing Lexical Resource and Using Sense-annotations of
OntoSenseNet for Sentiment Analysis | This paper illustrates the interface of the tool we developed for crowd
sourcing and we explain the annotation procedure in detail. Our tool is named
as 'Parupalli Padajaalam' which means web of words by Parupalli. The aim of
this tool is to populate the OntoSenseNet, sentiment polarity annotated Telugu
resource. Recent works have shown the importance of word-level annotations on
sentiment analysis. With this as basis, we aim to analyze the importance of
sense-annotations obtained from OntoSenseNet in performing the task of
sentiment analysis. We explain the fea- tures extracted from OntoSenseNet
(Telugu). Furthermore we compute and explain the adverbial class distribution
of verbs in OntoSenseNet. This task is known to aid in disambiguating
word-senses which helps in enhancing the performance of word-sense
disambiguation (WSD) task(s).
| 2,018 | Computation and Language |
A Sequence-to-Sequence Model for Semantic Role Labeling | We explore a novel approach for Semantic Role Labeling (SRL) by casting it as
a sequence-to-sequence process. We employ an attention-based model enriched
with a copying mechanism to ensure faithful regeneration of the input sequence,
while enabling interleaved generation of argument role labels. Here, we apply
this model in a monolingual setting, performing PropBank SRL on English
language data. The constrained sequence generation set-up enforced with the
copying mechanism allows us to analyze the performance and special properties
of the model on manually labeled data and benchmarking against state-of-the-art
sequence labeling models. We show that our model is able to solve the SRL
argument labeling task on English data, yet further structural decoding
constraints will need to be added to make the model truly competitive. Our work
represents a first step towards more advanced, generative SRL labeling setups.
| 2,018 | Computation and Language |
Constructing a Word Similarity Graph from Vector based Word
Representation for Named Entity Recognition | In this paper, we discuss a method for identifying a seed word that would
best represent a class of named entities in a graphical representation of words
and their similarities. Word networks, or word graphs, are representations of
vectorized text where nodes are the words encountered in a corpus, and the
weighted edges incident on the nodes represent how similar the words are to
each other. We intend to build a bilingual word graph and identify seed words
through community analysis that would be best used to segment a graph according
to its named entities, therefore providing an unsupervised way of tagging named
entities for a bilingual language base.
| 2,018 | Computation and Language |
Position-aware Self-attention with Relative Positional Encodings for
Slot Filling | This paper describes how to apply self-attention with relative positional
encodings to the task of relation extraction. We propose to use the
self-attention encoder layer together with an additional position-aware
attention layer that takes into account positions of the query and the object
in the sentence. The self-attention encoder also uses a custom implementation
of relative positional encodings which allow each word in the sentence to take
into account its left and right context. The evaluation of the model is done on
the TACRED dataset. The proposed model relies only on attention (no recurrent
or convolutional layers are used), while improving performance w.r.t. the
previous state of the art.
| 2,018 | Computation and Language |
A deep learning approach for understanding natural language commands for
mobile service robots | Using natural language to give instructions to robots is challenging, since
natural language understanding is still largely an open problem. In this paper
we address this problem by restricting our attention to commands modeled as one
action, plus arguments (also known as slots). For action detection (also called
intent detection) and slot filling various architectures of Recurrent Neural
Networks and Long Short Term Memory (LSTM) networks were evaluated, having
LSTMs achieved a superior accuracy. As the action requested may not fall within
the robots capabilities, a Support Vector Machine(SVM) is used to determine
whether it is or not. For the input of the neural networks, several word
embedding algorithms were compared. Finally, to implement the system in a
robot, a ROS package is created using a SMACH state machine. The proposed
system is then evaluated both using well-known datasets and benchmarks in the
context of domestic service robots.
| 2,018 | Computation and Language |
NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and
Online Learning | We present NMT-Keras, a flexible toolkit for training deep learning models,
which puts a particular emphasis on the development of advanced applications of
neural machine translation systems, such as interactive-predictive translation
protocols and long-term adaptation of the translation system via continuous
learning. NMT-Keras is based on an extended version of the popular Keras
library, and it runs on Theano and Tensorflow. State-of-the-art neural machine
translation models are deployed and used following the high-level framework
provided by Keras. Given its high modularity and flexibility, it also has been
extended to tackle different problems, such as image and video captioning,
sentence classification and visual question answering.
| 2,018 | Computation and Language |
Robust Text-to-SQL Generation with Execution-Guided Decoding | We consider the problem of neural semantic parsing, which translates natural
language questions into executable SQL queries. We introduce a new mechanism,
execution guidance, to leverage the semantics of SQL. It detects and excludes
faulty programs during the decoding procedure by conditioning on the execution
of partially generated program. The mechanism can be used with any
autoregressive generative model, which we demonstrate on four state-of-the-art
recurrent or template-based semantic parsing models. We demonstrate that
execution guidance universally improves model performance on various
text-to-SQL datasets with different scales and query complexity: WikiSQL, ATIS,
and GeoQuery. As a result, we achieve new state-of-the-art execution accuracy
of 83.8% on WikiSQL.
| 2,018 | Computation and Language |
Discriminating between Indo-Aryan Languages Using SVM Ensembles | In this paper we present a system based on SVM ensembles trained on
characters and words to discriminate between five similar languages of the
Indo-Aryan family: Hindi, Braj Bhasha, Awadhi, Bhojpuri, and Magahi. We
investigate the performance of individual features and combine the output of
single classifiers to maximize performance. The system competed in the
Indo-Aryan Language Identification (ILI) shared task organized within the
VarDial Evaluation Campaign 2018. Our best entry in the competition, named
ILIdentification, scored 88:95% F1 score and it was ranked 3rd out of 8 teams.
| 2,018 | Computation and Language |
Towards Better UD Parsing: Deep Contextualized Word Embeddings,
Ensemble, and Treebank Concatenation | This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared
task on Multilingual Parsing from Raw Text to Universal Dependencies. We base
our submission on Stanford's winning system for the CoNLL 2017 shared task and
make two effective extensions: 1) incorporating deep contextualized word
embeddings into both the part of speech tagger and parser; 2) ensembling
parsers trained with different initialization. We also explore different ways
of concatenating treebanks for further improvements. Experimental results on
the development data show the effectiveness of our methods. In the final
evaluation, our system was ranked first according to LAS (75.84%) and
outperformed the other systems by a large margin.
| 2,018 | Computation and Language |
On Training Recurrent Networks with Truncated Backpropagation Through
Time in Speech Recognition | Recurrent neural networks have been the dominant models for many speech and
language processing tasks. However, we understand little about the behavior and
the class of functions recurrent networks can realize. Moreover, the heuristics
used during training complicate the analyses. In this paper, we study recurrent
networks' ability to learn long-term dependency in the context of speech
recognition. We consider two decoding approaches, online and batch decoding,
and show the classes of functions to which the decoding approaches correspond.
We then draw a connection between batch decoding and a popular training
approach for recurrent networks, truncated backpropagation through time.
Changing the decoding approach restricts the amount of past history recurrent
networks can use for prediction, allowing us to analyze their ability to
remember. Empirically, we utilize long-term dependency in subphonetic states,
phonemes, and words, and show how the design decisions, such as the decoding
approach, lookahead, context frames, and consecutive prediction, characterize
the behavior of recurrent networks. Finally, we draw a connection between
Markov processes and vanishing gradients. These results have implications for
studying the long-term dependency in speech data and how these properties are
learned by recurrent networks.
| 2,018 | Computation and Language |
Detecting Levels of Depression in Text Based on Metrics | Depression is one of the most common and a major concern for society. Proper
monitoring using devices that can aid in its detection could be helpful to
prevent it all together. The Distress Analysis Interview Corpus (DAIC) is used
to build a metric-based depression detection. We have designed a metric to
describe the level of depression using negative sentences and classify the
participant accordingly. The score generated from the algorithm is then
levelled up to denote the intensity of depression. The results show that
measuring depression is very complex to using text alone as other factors are
not taken into consideration. Further, In the paper, the limitations of
measuring depression using text are described, and future suggestions are made.
| 2,018 | Computation and Language |
Jointly Embedding Entities and Text with Distant Supervision | Learning representations for knowledge base entities and concepts is becoming
increasingly important for NLP applications. However, recent entity embedding
methods have relied on structured resources that are expensive to create for
new domains and corpora. We present a distantly-supervised method for jointly
learning embeddings of entities and text from an unnanotated corpus, using only
a list of mappings between entities and surface forms. We learn embeddings from
open-domain and biomedical corpora, and compare against prior methods that rely
on human-annotated text or large knowledge graph structure. Our embeddings
capture entity similarity and relatedness better than prior work, both in
existing biomedical datasets and a new Wikipedia-based dataset that we release
to the community. Results on analogy completion and entity sense disambiguation
indicate that entities and words capture complementary information that can be
effectively combined for downstream use.
| 2,018 | Computation and Language |
Who is Killed by Police: Introducing Supervised Attention for
Hierarchical LSTMs | Finding names of people killed by police has become increasingly important as
police shootings get more and more public attention (police killing detection).
Unfortunately, there has been not much work in the literature addressing this
problem. The early work in this field \cite{keith2017identifying} proposed a
distant supervision framework based on Expectation Maximization (EM) to deal
with the multiple appearances of the names in documents. However, such EM-based
framework cannot take full advantages of deep learning models, necessitating
the use of hand-designed features to improve the detection performance. In this
work, we present a novel deep learning method to solve the problem of police
killing recognition. The proposed method relies on hierarchical LSTMs to model
the multiple sentences that contain the person names of interests, and
introduce supervised attention mechanisms based on semantical word lists and
dependency trees to upweight the important contextual words. Our experiments
demonstrate the benefits of the proposed model and yield the state-of-the-art
performance for police killing detection.
| 2,018 | Computation and Language |
Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme | In this paper, we propose a joint architecture that captures language, rhyme
and meter for sonnet modelling. We assess the quality of generated poems using
crowd and expert judgements. The stress and rhyme models perform very well, as
generated poems are largely indistinguishable from human-written poems. Expert
evaluation, however, reveals that a vanilla language model captures meter
implicitly, and that machine-generated poems still underperform in terms of
readability and emotion. Our research shows the importance expert evaluation
for poetry generation, and that future research should look beyond rhyme/meter
and focus on poetic language.
| 2,018 | Computation and Language |
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