Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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16,602 | Graph Convolutional Encoders for Syntax-aware Neural Machine Translation | cs.CL | We present a simple and effective approach to incorporating syntactic
structure into neural attention-based encoder-decoder models for machine
translation. We rely on graph-convolutional networks (GCNs), a recent class of
neural networks developed for modeling graph-structured data. Our GCNs use
predicted syntactic dep... | computer science |
16,603 | Towards String-to-Tree Neural Machine Translation | cs.CL | We present a simple method to incorporate syntactic information about the
target language in a neural machine translation system by translating into
linearized, lexicalized constituency trees. An experiment on the WMT16
German-English news translation task resulted in an improved BLEU score when
compared to a syntax-ag... | computer science |
16,604 | A Neural Architecture for Generating Natural Language Descriptions from
Source Code Changes | cs.CL | We propose a model to automatically describe changes introduced in the source
code of a program using natural language. Our method receives as input a set of
code commits, which contains both the modifications and message introduced by
an user. These two modalities are used to train an encoder-decoder
architecture. We ... | computer science |
16,605 | Learning Character-level Compositionality with Visual Features | cs.CL | Previous work has modeled the compositionality of words by creating
character-level models of meaning, reducing problems of sparsity for rare
words. However, in many writing systems compositionality has an effect even on
the character-level: the meaning of a character is derived by the sum of its
parts. In this paper, ... | computer science |
16,606 | Deep Joint Entity Disambiguation with Local Neural Attention | cs.CL | We propose a novel deep learning model for joint document-level entity
disambiguation, which leverages learned neural representations. Key components
are entity embeddings, a neural attention mechanism over local context windows,
and a differentiable joint inference stage for disambiguation. Our approach
thereby combin... | computer science |
16,607 | Automatic Disambiguation of French Discourse Connectives | cs.CL | Discourse connectives (e.g. however, because) are terms that can explicitly
convey a discourse relation within a text. While discourse connectives have
been shown to be an effective clue to automatically identify discourse
relations, they are not always used to convey such relations, thus they should
first be disambigu... | computer science |
16,608 | SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine | cs.CL | We publicly release a new large-scale dataset, called SearchQA, for machine
comprehension, or question-answering. Unlike recently released datasets, such
as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to
reflect a full pipeline of general question-answering. That is, we start not
from an exi... | computer science |
16,609 | Sentiment analysis based on rhetorical structure theory: Learning deep
neural networks from discourse trees | cs.CL | Prominent applications of sentiment analysis are countless, covering areas
such as marketing, customer service and communication. The conventional
bag-of-words approach for measuring sentiment merely counts term frequencies;
however, it neglects the position of the terms within the discourse. As a
remedy, we develop a ... | computer science |
16,610 | Baselines and test data for cross-lingual inference | cs.CL | The recent years have seen a revival of interest in textual entailment,
sparked by i) the emergence of powerful deep neural network learners for
natural language processing and ii) the timely development of large-scale
evaluation datasets such as SNLI. Recast as natural language inference, the
problem now amounts to de... | computer science |
16,611 | Representing Sentences as Low-Rank Subspaces | cs.CL | Sentences are important semantic units of natural language. A generic,
distributional representation of sentences that can capture the latent
semantics is beneficial to multiple downstream applications. We observe a
simple geometry of sentences -- the word representations of a given sentence
(on average 10.23 words in ... | computer science |
16,612 | An Empirical Analysis of NMT-Derived Interlingual Embeddings and their
Use in Parallel Sentence Identification | cs.CL | End-to-end neural machine translation has overtaken statistical machine
translation in terms of translation quality for some language pairs, specially
those with large amounts of parallel data. Besides this palpable improvement,
neural networks provide several new properties. A single system can be trained
to translate... | computer science |
16,613 | A Broad-Coverage Challenge Corpus for Sentence Understanding through
Inference | cs.CL | This paper introduces the Multi-Genre Natural Language Inference (MultiNLI)
corpus, a dataset designed for use in the development and evaluation of machine
learning models for sentence understanding. In addition to being one of the
largest corpora available for the task of NLI, at 433k examples, this corpus
improves up... | computer science |
16,614 | Predicting Role Relevance with Minimal Domain Expertise in a Financial
Domain | cs.CL | Word embeddings have made enormous inroads in recent years in a wide variety
of text mining applications. In this paper, we explore a word embedding-based
architecture for predicting the relevance of a role between two financial
entities within the context of natural language sentences. In this extended
abstract, we pr... | computer science |
16,615 | Dependency resolution and semantic mining using Tree Adjoining Grammars
for Tamil Language | cs.CL | Tree adjoining grammars (TAGs) provide an ample tool to capture syntax of
many Indian languages. Tamil represents a special challenge to computational
formalisms as it has extensive agglutinative morphology and a comparatively
difficult argument structure. Modelling Tamil syntax and morphology using TAG
is an interesti... | computer science |
16,616 | Adversarial Multi-task Learning for Text Classification | cs.CL | Neural network models have shown their promising opportunities for multi-task
learning, which focus on learning the shared layers to extract the common and
task-invariant features. However, in most existing approaches, the extracted
shared features are prone to be contaminated by task-specific features or the
noise bro... | computer science |
16,617 | Redefining Context Windows for Word Embedding Models: An Experimental
Study | cs.CL | Distributional semantic models learn vector representations of words through
the contexts they occur in. Although the choice of context (which often takes
the form of a sliding window) has a direct influence on the resulting
embeddings, the exact role of this model component is still not fully
understood. This paper pr... | computer science |
16,618 | Global Relation Embedding for Relation Extraction | cs.CL | Recent studies have shown that embedding textual relations using deep neural
networks greatly helps relation extraction. However, many existing studies rely
on supervised learning; their performance is dramatically limited by the
availability of training data. In this work, we generalize textual relation
embedding to t... | computer science |
16,619 | Cross-domain Semantic Parsing via Paraphrasing | cs.CL | Existing studies on semantic parsing mainly focus on the in-domain setting.
We formulate cross-domain semantic parsing as a domain adaptation problem:
train a semantic parser on some source domains and then adapt it to the target
domain. Due to the diversity of logical forms in different domains, this
problem presents ... | computer science |
16,620 | Neural End-to-End Learning for Computational Argumentation Mining | cs.CL | We investigate neural techniques for end-to-end computational argumentation
mining (AM). We frame AM both as a token-based dependency parsing and as a
token-based sequence tagging problem, including a multi-task learning setup.
Contrary to models that operate on the argument component level, we find that
framing AM as ... | computer science |
16,621 | Reinforcement Learning with External Knowledge and Two-Stage Q-functions
for Predicting Popular Reddit Threads | cs.CL | This paper addresses the problem of predicting popularity of comments in an
online discussion forum using reinforcement learning, particularly addressing
two challenges that arise from having natural language state and action spaces.
First, the state representation, which characterizes the history of comments
tracked i... | computer science |
16,622 | SwellShark: A Generative Model for Biomedical Named Entity Recognition
without Labeled Data | cs.CL | We present SwellShark, a framework for building biomedical named entity
recognition (NER) systems quickly and without hand-labeled data. Our approach
views biomedical resources like lexicons as function primitives for
autogenerating weak supervision. We then use a generative model to unify and
denoise this supervision ... | computer science |
16,623 | Improving Context Aware Language Models | cs.CL | Increased adaptability of RNN language models leads to improved predictions
that benefit many applications. However, current methods do not take full
advantage of the RNN structure. We show that the most widely-used approach to
adaptation (concatenating the context with the word embedding at the input to
the recurrent ... | computer science |
16,624 | Neural System Combination for Machine Translation | cs.CL | Neural machine translation (NMT) becomes a new approach to machine
translation and generates much more fluent results compared to statistical
machine translation (SMT).
However, SMT is usually better than NMT in translation adequacy. It is
therefore a promising direction to combine the advantages of both NMT and SMT.... | computer science |
16,625 | Improving Semantic Composition with Offset Inference | cs.CL | Count-based distributional semantic models suffer from sparsity due to
unobserved but plausible co-occurrences in any text collection. This problem is
amplified for models like Anchored Packed Trees (APTs), that take the
grammatical type of a co-occurrence into account. We therefore introduce a
novel form of distributi... | computer science |
16,626 | Lexical Features in Coreference Resolution: To be Used With Caution | cs.CL | Lexical features are a major source of information in state-of-the-art
coreference resolvers. Lexical features implicitly model some of the linguistic
phenomena at a fine granularity level. They are especially useful for
representing the context of mentions. In this paper we investigate a drawback
of using many lexical... | computer science |
16,627 | Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual
Machine Translation | cs.CL | Sarcasm is a form of speech in which speakers say the opposite of what they
truly mean in order to convey a strong sentiment. In other words, "Sarcasm is
the giant chasm between what I say, and the person who doesn't get it.". In
this paper we present the novel task of sarcasm interpretation, defined as the
generation ... | computer science |
16,628 | Medical Text Classification using Convolutional Neural Networks | cs.CL | We present an approach to automatically classify clinical text at a sentence
level. We are using deep convolutional neural networks to represent complex
features. We train the network on a dataset providing a broad categorization of
health information. Through a detailed evaluation, we demonstrate that our
method outpe... | computer science |
16,629 | Affect-LM: A Neural Language Model for Customizable Affective Text
Generation | cs.CL | Human verbal communication includes affective messages which are conveyed
through use of emotionally colored words. There has been a lot of research in
this direction but the problem of integrating state-of-the-art neural language
models with affective information remains an area ripe for exploration. In this
paper, we... | computer science |
16,630 | Deep Multitask Learning for Semantic Dependency Parsing | cs.CL | We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency pa... | computer science |
16,631 | Argument Mining with Structured SVMs and RNNs | cs.CL | We propose a novel factor graph model for argument mining, designed for
settings in which the argumentative relations in a document do not necessarily
form a tree structure. (This is the case in over 20% of the web comments
dataset we release.) Our model jointly learns elementary unit type
classification and argumentat... | computer science |
16,632 | Deep Keyphrase Generation | cs.CL | Keyphrase provides highly-summative information that can be effectively used
for understanding, organizing and retrieving text content. Though previous
studies have provided many workable solutions for automated keyphrase
extraction, they commonly divided the to-be-summarized content into multiple
text chunks, then ran... | computer science |
16,633 | Neural Machine Translation via Binary Code Prediction | cs.CL | In this paper, we propose a new method for calculating the output layer in
neural machine translation systems. The method is based on predicting a binary
code for each word and can reduce computation time/memory requirements of the
output layer to be logarithmic in vocabulary size in the best case. In
addition, we also... | computer science |
16,634 | A* CCG Parsing with a Supertag and Dependency Factored Model | cs.CL | We propose a new A* CCG parsing model in which the probability of a tree is
decomposed into factors of CCG categories and its syntactic dependencies both
defined on bi-directional LSTMs. Our factored model allows the precomputation
of all probabilities and runs very efficiently, while modeling sentence
structures expli... | computer science |
16,635 | Learning to Create and Reuse Words in Open-Vocabulary Neural Language
Modeling | cs.CL | Fixed-vocabulary language models fail to account for one of the most
characteristic statistical facts of natural language: the frequent creation and
reuse of new word types. Although character-level language models offer a
partial solution in that they can create word types not attested in the
training corpus, they do ... | computer science |
16,636 | Fast and Accurate Neural Word Segmentation for Chinese | cs.CL | Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with ... | computer science |
16,637 | Selective Encoding for Abstractive Sentence Summarization | cs.CL | We propose a selective encoding model to extend the sequence-to-sequence
framework for abstractive sentence summarization. It consists of a sentence
encoder, a selective gate network, and an attention equipped decoder. The
sentence encoder and decoder are built with recurrent neural networks. The
selective gate network... | computer science |
16,638 | Robust Incremental Neural Semantic Graph Parsing | cs.CL | Parsing sentences to linguistically-expressive semantic representations is a
key goal of Natural Language Processing. Yet statistical parsing has focused
almost exclusively on bilexical dependencies or domain-specific logical forms.
We propose a neural encoder-decoder transition-based parser which is the first
full-cov... | computer science |
16,639 | Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge
Graph Embeddings | cs.CL | We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhi... | computer science |
16,640 | Lexically Constrained Decoding for Sequence Generation Using Grid Beam
Search | cs.CL | We present Grid Beam Search (GBS), an algorithm which extends beam search to
allow the inclusion of pre-specified lexical constraints. The algorithm can be
used with any model that generates a sequence $ \mathbf{\hat{y}} =
\{y_{0}\ldots y_{T}\} $, by maximizing $ p(\mathbf{y} | \mathbf{x}) =
\prod\limits_{t}p(y_{t} | \... | computer science |
16,641 | Found in Translation: Reconstructing Phylogenetic Language Trees from
Translations | cs.CL | Translation has played an important role in trade, law, commerce, politics,
and literature for thousands of years. Translators have always tried to be
invisible; ideal translations should look as if they were written originally in
the target language. We show that traces of the source language remain in the
translation... | computer science |
16,642 | Watset: Automatic Induction of Synsets from a Graph of Synonyms | cs.CL | This paper presents a new graph-based approach that induces synsets using
synonymy dictionaries and word embeddings. First, we build a weighted graph of
synonyms extracted from commonly available resources, such as Wiktionary.
Second, we apply word sense induction to deal with ambiguous words. Finally, we
cluster the d... | computer science |
16,643 | What is the Essence of a Claim? Cross-Domain Claim Identification | cs.CL | Argument mining has become a popular research area in NLP. It typically
includes the identification of argumentative components, e.g. claims, as the
central component of an argument. We perform a qualitative analysis across six
different datasets and show that these appear to conceptualize claims quite
differently. To ... | computer science |
16,644 | A Trie-Structured Bayesian Model for Unsupervised Morphological
Segmentation | cs.CL | In this paper, we introduce a trie-structured Bayesian model for unsupervised
morphological segmentation. We adopt prior information from different sources
in the model. We use neural word embeddings to discover words that are
morphologically derived from each other and thereby that are semantically
similar. We use let... | computer science |
16,645 | Predicting Native Language from Gaze | cs.CL | A fundamental question in language learning concerns the role of a speaker's
first language in second language acquisition. We present a novel methodology
for studying this question: analysis of eye-movement patterns in second
language reading of free-form text. Using this methodology, we demonstrate for
the first time... | computer science |
16,646 | Ruminating Reader: Reasoning with Gated Multi-Hop Attention | cs.CL | To answer the question in machine comprehension (MC) task, the models need to
establish the interaction between the question and the context. To tackle the
problem that the single-pass model cannot reflect on and correct its answer, we
present Ruminating Reader. Ruminating Reader adds a second pass of attention
and a n... | computer science |
16,647 | Recognizing Descriptive Wikipedia Categories for Historical Figures | cs.CL | Wikipedia is a useful knowledge source that benefits many applications in
language processing and knowledge representation. An important feature of
Wikipedia is that of categories. Wikipedia pages are assigned different
categories according to their contents as human-annotated labels which can be
used in information re... | computer science |
16,648 | A Challenge Set Approach to Evaluating Machine Translation | cs.CL | Neural machine translation represents an exciting leap forward in translation
quality. But what longstanding weaknesses does it resolve, and which remain? We
address these questions with a challenge set approach to translation evaluation
and error analysis. A challenge set consists of a small set of sentences, each
han... | computer science |
16,649 | Detecting English Writing Styles For Non Native Speakers | cs.CL | This paper presents the first attempt, up to our knowledge, to classify
English writing styles on this scale with the challenge of classifying day to
day language written by writers with different backgrounds covering various
areas of topics.The paper proposes simple machine learning algorithms and
simple to generate f... | computer science |
16,650 | Streaming Word Embeddings with the Space-Saving Algorithm | cs.CL | We develop a streaming (one-pass, bounded-memory) word embedding algorithm
based on the canonical skip-gram with negative sampling algorithm implemented
in word2vec. We compare our streaming algorithm to word2vec empirically by
measuring the cosine similarity between word pairs under each algorithm and by
applying each... | computer science |
16,651 | Adversarial Multi-Criteria Learning for Chinese Word Segmentation | cs.CL | Different linguistic perspectives causes many diverse segmentation criteria
for Chinese word segmentation (CWS). Most existing methods focus on improve the
performance for each single criterion. However, it is interesting to exploit
these different criteria and mining their common underlying knowledge. In this
paper, w... | computer science |
16,652 | Joint POS Tagging and Dependency Parsing with Transition-based Neural
Networks | cs.CL | While part-of-speech (POS) tagging and dependency parsing are observed to be
closely related, existing work on joint modeling with manually crafted feature
templates suffers from the feature sparsity and incompleteness problems. In
this paper, we propose an approach to joint POS tagging and dependency parsing
using tra... | computer science |
16,653 | Automatic Compositor Attribution in the First Folio of Shakespeare | cs.CL | Compositor attribution, the clustering of pages in a historical printed
document by the individual who set the type, is a bibliographic task that
relies on analysis of orthographic variation and inspection of visual details
of the printed page. In this paper, we introduce a novel unsupervised model
that jointly describ... | computer science |
16,654 | Other Topics You May Also Agree or Disagree: Modeling Inter-Topic
Preferences using Tweets and Matrix Factorization | cs.CL | We present in this paper our approach for modeling inter-topic preferences of
Twitter users: for example, those who agree with the Trans-Pacific Partnership
(TPP) also agree with free trade. This kind of knowledge is useful not only for
stance detection across multiple topics but also for various real-world
application... | computer science |
16,655 | Topically Driven Neural Language Model | cs.CL | Language models are typically applied at the sentence level, without access
to the broader document context. We present a neural language model that
incorporates document context in the form of a topic model-like architecture,
thus providing a succinct representation of the broader document context
outside of the curre... | computer science |
16,656 | Riemannian Optimization for Skip-Gram Negative Sampling | cs.CL | Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its
implementation in "word2vec" software, is usually optimized by stochastic
gradient descent. However, the optimization of SGNS objective can be viewed as
a problem of searching for a good matrix with the low-rank constraint. The most
standard way... | computer science |
16,657 | Enriching Complex Networks with Word Embeddings for Detecting Mild
Cognitive Impairment from Speech Transcripts | cs.CL | Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose.
Linguistic features, mainly from parsers, have been used to detect MCI, but
this is not suitable for large-scale assessments. MCI disfluencies produce
non-grammatical speech that requires manual or high precision automatic
correction of transcr... | computer science |
16,658 | Diversity driven Attention Model for Query-based Abstractive
Summarization | cs.CL | Abstractive summarization aims to generate a shorter version of the document
covering all the salient points in a compact and coherent fashion. On the other
hand, query-based summarization highlights those points that are relevant in
the context of a given query. The encode-attend-decode paradigm has achieved
notable s... | computer science |
16,659 | From Characters to Words to in Between: Do We Capture Morphology? | cs.CL | Words can be represented by composing the representations of subword units
such as word segments, characters, and/or character n-grams. While such
representations are effective and may capture the morphological regularities of
words, they have not been systematically compared, and it is not understood how
they interact... | computer science |
16,660 | Neural AMR: Sequence-to-Sequence Models for Parsing and Generation | cs.CL | Sequence-to-sequence models have shown strong performance across a broad
range of applications. However, their application to parsing and generating
text usingAbstract Meaning Representation (AMR)has been limited, due to the
relatively limited amount of labeled data and the non-sequential nature of the
AMR graphs. We p... | computer science |
16,661 | Question Answering on Knowledge Bases and Text using Universal Schema
and Memory Networks | cs.CL | Existing question answering methods infer answers either from a knowledge
base or from raw text. While knowledge base (KB) methods are good at answering
compositional questions, their performance is often affected by the
incompleteness of the KB. Au contraire, web text contains millions of facts
that are absent in the ... | computer science |
16,662 | Learning Structured Natural Language Representations for Semantic
Parsing | cs.CL | We introduce a neural semantic parser that converts natural language
utterances to intermediate representations in the form of predicate-argument
structures, which are induced with a transition system and subsequently mapped
to target domains. The semantic parser is trained end-to-end using annotated
logical forms or t... | computer science |
16,663 | Duluth at Semeval-2017 Task 7 : Puns upon a midnight dreary, Lexical
Semantics for the weak and weary | cs.CL | This paper describes the Duluth systems that participated in SemEval-2017
Task 7 : Detection and Interpretation of English Puns. The Duluth systems
participated in all three subtasks, and relied on methods that included word
sense disambiguation and measures of semantic relatedness. | computer science |
16,664 | Duluth at SemEval-2017 Task 6: Language Models in Humor Detection | cs.CL | This paper describes the Duluth system that participated in SemEval-2017 Task
6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks
A and B using N-gram language models, ranking highly in the task evaluation.
This paper discusses the results of our system in the development and
evaluation stage... | computer science |
16,665 | A GRU-Gated Attention Model for Neural Machine Translation | cs.CL | Neural machine translation (NMT) heavily relies on an attention network to
produce a context vector for each target word prediction. In practice, we find
that context vectors for different target words are quite similar to one
another and therefore are insufficient in discriminatively predicting target
words. The reaso... | computer science |
16,666 | A Survey of Neural Network Techniques for Feature Extraction from Text | cs.CL | This paper aims to catalyze the discussions about text feature extraction
techniques using neural network architectures. The research questions discussed
in the paper focus on the state-of-the-art neural network techniques that have
proven to be useful tools for language processing, language generation, text
classifica... | computer science |
16,667 | Learning a Neural Semantic Parser from User Feedback | cs.CL | We present an approach to rapidly and easily build natural language
interfaces to databases for new domains, whose performance improves over time
based on user feedback, and requires minimal intervention. To achieve this, we
adapt neural sequence models to map utterances directly to SQL with its full
expressivity, bypa... | computer science |
16,668 | Mapping Instructions and Visual Observations to Actions with
Reinforcement Learning | cs.CL | We propose to directly map raw visual observations and text input to actions
for instruction execution. While existing approaches assume access to
structured environment representations or use a pipeline of separately trained
models, we learn a single model to jointly reason about linguistic and visual
input. We use re... | computer science |
16,669 | Word Affect Intensities | cs.CL | Words often convey affect -- emotions, feelings, and attitudes. Lexicons of
word-affect association have applications in automatic emotion analysis and
natural language generation. However, existing lexicons indicate only coarse
categories of affect association. Here, for the first time, we create an affect
intensity l... | computer science |
16,670 | How compatible are our discourse annotations? Insights from mapping
RST-DT and PDTB annotations | cs.CL | Discourse-annotated corpora are an important resource for the community, but
they are often annotated according to different frameworks. This makes
comparison of the annotations difficult, thereby also preventing researchers
from searching the corpora in a unified way, or using all annotated data
jointly to train compu... | computer science |
16,671 | Neural Word Segmentation with Rich Pretraining | cs.CL | Neural word segmentation research has benefited from large-scale raw texts by
leveraging them for pretraining character and word embeddings. On the other
hand, statistical segmentation research has exploited richer sources of
external information, such as punctuation, automatic segmentation and POS. We
investigate the ... | computer science |
16,672 | Understanding and Detecting Supporting Arguments of Diverse Types | cs.CL | We investigate the problem of sentence-level supporting argument detection
from relevant documents for user-specified claims. A dataset containing claims
and associated citation articles is collected from online debate website
idebate.org. We then manually label sentence-level supporting arguments from
the documents al... | computer science |
16,673 | Semi-supervised sequence tagging with bidirectional language models | cs.CL | Pre-trained word embeddings learned from unlabeled text have become a
standard component of neural network architectures for NLP tasks. However, in
most cases, the recurrent network that operates on word-level representations
to produce context sensitive representations is trained on relatively little
labeled data. In ... | computer science |
16,674 | Lifelong Learning CRF for Supervised Aspect Extraction | cs.CL | This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain marked... | computer science |
16,675 | A Conditional Variational Framework for Dialog Generation | cs.CL | Deep latent variable models have been shown to facilitate the response
generation for open-domain dialog systems. However, these latent variables are
highly randomized, leading to uncontrollable generated responses. In this
paper, we propose a framework allowing conditional response generation based on
specific attribu... | computer science |
16,676 | Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings | cs.CL | We consider the problem of learning general-purpose, paraphrastic sentence
embeddings, revisiting the setting of Wieting et al. (2016b). While they found
LSTM recurrent networks to underperform word averaging, we present several
developments that together produce the opposite conclusion. These include
training on sente... | computer science |
16,677 | Duluth at SemEval--2016 Task 14 : Extending Gloss Overlaps to Enrich
Semantic Taxonomies | cs.CL | This paper describes the Duluth systems that participated in Task 14 of
SemEval 2016, Semantic Taxonomy Enrichment. There were three related systems in
the formal evaluation which are discussed here, along with numerous
post--evaluation runs. All of these systems identified synonyms between WordNet
and other dictionari... | computer science |
16,678 | Dependency Parsing with Dilated Iterated Graph CNNs | cs.CL | Dependency parses are an effective way to inject linguistic knowledge into
many downstream tasks, and many practitioners wish to efficiently parse
sentences at scale. Recent advances in GPU hardware have enabled neural
networks to achieve significant gains over the previous best models, these
models still fail to lever... | computer science |
16,679 | Model Transfer for Tagging Low-resource Languages using a Bilingual
Dictionary | cs.CL | Cross-lingual model transfer is a compelling and popular method for
predicting annotations in a low-resource language, whereby parallel corpora
provide a bridge to a high-resource language and its associated annotated
corpora. However, parallel data is not readily available for many languages,
limiting the applicabilit... | computer science |
16,680 | Data Augmentation for Low-Resource Neural Machine Translation | cs.CL | The quality of a Neural Machine Translation system depends substantially on
the availability of sizable parallel corpora. For low-resource language pairs
this is not the case, resulting in poor translation quality. Inspired by work
in computer vision, we propose a novel data augmentation approach that targets
low-frequ... | computer science |
16,681 | Learning Topic-Sensitive Word Representations | cs.CL | Distributed word representations are widely used for modeling words in NLP
tasks. Most of the existing models generate one representation per word and do
not consider different meanings of a word. We present two approaches to learn
multiple topic-sensitive representations per word by using Hierarchical
Dirichlet Proces... | computer science |
16,682 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter:
predicting sentiment from financial news headlines | cs.CL | This paper describes our participation in Task 5 track 2 of SemEval 2017 to
predict the sentiment of financial news headlines for a specific company on a
continuous scale between -1 and 1. We tackled the problem using a number of
approaches, utilising a Support Vector Regression (SVR) and a Bidirectional
Long Short-Ter... | computer science |
16,683 | Efficient Natural Language Response Suggestion for Smart Reply | cs.CL | This paper presents a computationally efficient machine-learned method for
natural language response suggestion. Feed-forward neural networks using n-gram
embedding features encode messages into vectors which are optimized to give
message-response pairs a high dot-product value. An optimized search finds
response sugge... | computer science |
16,684 | Chat Detection in an Intelligent Assistant: Combining Task-oriented and
Non-task-oriented Spoken Dialogue Systems | cs.CL | Recently emerged intelligent assistants on smartphones and home electronics
(e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific
task-oriented spoken dialogue systems and open-domain non-task-oriented ones.
To realize such hybrid dialogue systems, this paper investigates determining
whether or not a u... | computer science |
16,685 | A Teacher-Student Framework for Zero-Resource Neural Machine Translation | cs.CL | While end-to-end neural machine translation (NMT) has made remarkable
progress recently, it still suffers from the data scarcity problem for
low-resource language pairs and domains. In this paper, we propose a method for
zero-resource NMT by assuming that parallel sentences have close probabilities
of generating a sent... | computer science |
16,686 | Modeling Source Syntax for Neural Machine Translation | cs.CL | Even though a linguistics-free sequence to sequence model in neural machine
translation (NMT) has certain capability of implicitly learning syntactic
information of source sentences, this paper shows that source syntax can be
explicitly incorporated into NMT effectively to provide further improvements.
Specifically, we... | computer science |
16,687 | Entity Linking with people entity on Wikipedia | cs.CL | This paper introduces a new model that uses named entity recognition,
coreference resolution, and entity linking techniques, to approach the task of
linking people entities on Wikipedia people pages to their corresponding
Wikipedia pages if applicable. Our task is different from general and
traditional entity linking b... | computer science |
16,688 | A Hybrid Architecture for Multi-Party Conversational Systems | cs.CL | Multi-party Conversational Systems are systems with natural language
interaction between one or more people or systems. From the moment that an
utterance is sent to a group, to the moment that it is replied in the group by
a member, several activities must be done by the system: utterance
understanding, information sea... | computer science |
16,689 | On the effectiveness of feature set augmentation using clusters of word
embeddings | cs.CL | Word clusters have been empirically shown to offer important performance
improvements on various tasks. Despite their importance, their incorporation in
the standard pipeline of feature engineering relies more on a trial-and-error
procedure where one evaluates several hyper-parameters, like the number of
clusters to be... | computer science |
16,690 | Chunk-Based Bi-Scale Decoder for Neural Machine Translation | cs.CL | In typical neural machine translation~(NMT), the decoder generates a sentence
word by word, packing all linguistic granularities in the same time-scale of
RNN. In this paper, we propose a new type of decoder for NMT, which splits the
decode state into two parts and updates them in two different time-scales.
Specificall... | computer science |
16,691 | Probabilistic Typology: Deep Generative Models of Vowel Inventories | cs.CL | Linguistic typology studies the range of structures present in human
language. The main goal of the field is to discover which sets of possible
phenomena are universal, and which are merely frequent. For example, all
languages have vowels, while most---but not all---languages have an /u/ sound.
In this paper we present... | computer science |
16,692 | A Finite State and Rule-based Akshara to Prosodeme (A2P) Converter in
Hindi | cs.CL | This article describes a software module called Akshara to Prosodeme (A2P)
converter in Hindi. It converts an input grapheme into prosedeme (sequence of
phonemes with the specification of syllable boundaries and prosodic labels).
The software is based on two proposed finite state machines\textemdash one for
the syllabi... | computer science |
16,693 | Sharp Models on Dull Hardware: Fast and Accurate Neural Machine
Translation Decoding on the CPU | cs.CL | Attentional sequence-to-sequence models have become the new standard for
machine translation, but one challenge of such models is a significant increase
in training and decoding cost compared to phrase-based systems. Here, we focus
on efficient decoding, with a goal of achieving accuracy close the
state-of-the-art in n... | computer science |
16,694 | Machine Comprehension by Text-to-Text Neural Question Generation | cs.CL | We propose a recurrent neural model that generates natural-language questions
from documents, conditioned on answers. We show how to train the model using a
combination of supervised and reinforcement learning. After teacher forcing for
standard maximum likelihood training, we fine-tune the model using policy
gradient ... | computer science |
16,695 | Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters
for Tweet Polarity Classification | cs.CL | This paper presents Senti17 system which uses ten convolutional neural
networks (ConvNet) to assign a sentiment label to a tweet. The network consists
of a convolutional layer followed by a fully-connected layer and a Softmax on
top. Ten instances of this network are initialized with the same word
embeddings as inputs ... | computer science |
16,696 | Cross-lingual Distillation for Text Classification | cs.CL | Cross-lingual text classification(CLTC) is the task of classifying documents
written in different languages into the same taxonomy of categories. This paper
presents a novel approach to CLTC that builds on model distillation, which
adapts and extends a framework originally proposed for model compression. Using
soft pro... | computer science |
16,697 | Crowdsourcing Argumentation Structures in Chinese Hotel Reviews | cs.CL | Argumentation mining aims at automatically extracting the premises-claim
discourse structures in natural language texts. There is a great demand for
argumentation corpora for customer reviews. However, due to the controversial
nature of the argumentation annotation task, there exist very few large-scale
argumentation c... | computer science |
16,698 | Joint RNN Model for Argument Component Boundary Detection | cs.CL | Argument Component Boundary Detection (ACBD) is an important sub-task in
argumentation mining; it aims at identifying the word sequences that constitute
argument components, and is usually considered as the first sub-task in the
argumentation mining pipeline. Existing ACBD methods heavily depend on
task-specific knowle... | computer science |
16,699 | Deep Speaker: an End-to-End Neural Speaker Embedding System | cs.CL | We present Deep Speaker, a neural speaker embedding system that maps
utterances to a hypersphere where speaker similarity is measured by cosine
similarity. The embeddings generated by Deep Speaker can be used for many
tasks, including speaker identification, verification, and clustering. We
experiment with ResCNN and G... | computer science |
16,700 | Building Morphological Chains for Agglutinative Languages | cs.CL | In this paper, we build morphological chains for agglutinative languages by
using a log-linear model for the morphological segmentation task. The model is
based on the unsupervised morphological segmentation system called
MorphoChains. We extend MorphoChains log linear model by expanding the
candidate space recursively... | computer science |
16,701 | Supervised Learning of Universal Sentence Representations from Natural
Language Inference Data | cs.CL | Many modern NLP systems rely on word embeddings, previously trained in an
unsupervised manner on large corpora, as base features. Efforts to obtain
embeddings for larger chunks of text, such as sentences, have however not been
so successful. Several attempts at learning unsupervised representations of
sentences have no... | computer science |
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