pwc-archive/papers-with-abstracts huggingface dataset:
Cross-lingual Name Tagging and Linking for 282 Languages
Authors: Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, Heng Ji
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Translation, Word Translation

The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.
Adversarial Training for Unsupervised Bilingual Lexicon Induction
Authors: Meng Zhang, Yang Liu, Huanbo Luan, Maosong Sun
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Bilingual Lexicon Induction, Form, Word Embeddings

Word embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon. In this work, we show that such cross-lingual connection can actually be established without any form of supervision. We achieve this end by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. We carry out evaluation on the unsupervised bilingual lexicon induction task. Even though this task appears intrinsically cross-lingual, we are able to demonstrate encouraging performance without any cross-lingual clues.
Estimating Code-Switching on Twitter with a Novel Generalized Word-Level Language Detection Technique
Authors: Shruti Rijhwani, Royal Sequiera, Monojit Choudhury, Kalika Bali, Ch Maddila, ra Shekhar
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentence

Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence. Existing models are restricted to code-switching between two specific languages and fail in real-world scenarios as text input rarely has a priori information on the languages used. We present a novel unsupervised word-level language detection technique for code-switched text for an arbitrarily large number of languages, which does not require any manually annotated training data. Our experiments with tweets in seven languages show a 74{\%} relative error reduction in word-level labeling with respect to competitive baselines. We then use this system to conduct a large-scale quantitative analysis of code-switching patterns on Twitter, both global as well as region-specific, with 58M tweets.
Improved Word Representation Learning with Sememes
Authors: Yilin Niu, Ruobing Xie, Zhiyuan Liu, Maosong Sun
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Common Sense Reasoning, Language Modeling, Language Modelling, Machine Translation, Representation Learning, Sentiment Analysis, Word Embeddings, Word Sense Disambiguation, Word Sense Induction, Word Similarity

Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people manually annotate word sememes and form linguistic common-sense knowledge bases. In this paper, we present that, word sememe information can improve word representation learning (WRL), which maps words into a low-dimensional semantic space and serves as a fundamental step for many NLP tasks. The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately. More specifically, we follow the framework of Skip-gram and present three sememe-encoded models to learn representations of sememes, senses and words, where we apply the attention scheme to detect word senses in various contexts. We conduct experiments on two tasks including word similarity and word analogy, and our models significantly outperform baselines. The results indicate that WRL can benefit from sememes via the attention scheme, and also confirm our models being capable of correctly modeling sememe information.
A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data
Authors: Qiaolin Xia, Lei Sha, Baobao Chang, Zhifang Sui
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Chinese Semantic Role Labeling, Machine Translation, Semantic Role Labeling, Word Embeddings

Previous studies on Chinese semantic role labeling (SRL) have concentrated on a single semantically annotated corpus. But the training data of single corpus is often limited. Whereas the other existing semantically annotated corpora for Chinese SRL are scattered across different annotation frameworks. But still, Data sparsity remains a bottleneck. This situation calls for larger training datasets, or effective approaches which can take advantage of highly heterogeneous data. In this paper, we focus mainly on the latter, that is, to improve Chinese SRL by using heterogeneous corpora together. We propose a novel progressive learning model which augments the Progressive Neural Network with Gated Recurrent Adapters. The model can accommodate heterogeneous inputs and effectively transfer knowledge between them. We also release a new corpus, Chinese SemBank, for Chinese SRL. Experiments on CPB 1.0 show that our model outperforms state-of-the-art methods.
Identifying 1950s American Jazz Musicians: Fine-Grained IsA Extraction via Modifier Composition
Authors: Ellie Pavlick, Marius Pa{\c{s}}ca
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

We present a method for populating fine-grained classes (e.g., {``}1950s American jazz musicians{''}) with instances (e.g., Charles Mingus ). While state-of-the-art methods tend to treat class labels as single lexical units, the proposed method considers each of the individual modifiers in the class label relative to the head. An evaluation on the task of reconstructing Wikipedia category pages demonstrates a {\textgreater}10 point increase in AUC, over a strong baseline relying on widely-used Hearst patterns.
Parsing to 1-Endpoint-Crossing, Pagenumber-2 Graphs
Authors: Junjie Cao, Sheng Huang, Weiwei Sun, Xiaojun Wan
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing, Semantic Dependency Parsing

We study the Maximum Subgraph problem in deep dependency parsing. We consider two restrictions to deep dependency graphs: (a) 1-endpoint-crossing and (b) pagenumber-2. Our main contribution is an exact algorithm that obtains maximum subgraphs satisfying both restrictions simultaneously in time O(n5). Moreover, ignoring one linguistically-rare structure descreases the complexity to O(n4). We also extend our quartic-time algorithm into a practical parser with a discriminative disambiguation model and evaluate its performance on four linguistic data sets used in semantic dependency parsing.
Semantic Parsing of Pre-university Math Problems
Authors: Takuya Matsuzaki, Takumi Ito, Hidenao Iwane, Hirokazu Anai, Noriko H. Arai
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Math, Semantic Parsing

We have been developing an end-to-end math problem solving system that accepts natural language input. The current paper focuses on how we analyze the problem sentences to produce logical forms. We chose a hybrid approach combining a shallow syntactic analyzer and a manually-developed lexicalized grammar. A feature of the grammar is that it is extensively typed on the basis of a formal ontology for pre-university math. These types are helpful in semantic disambiguation inside and across sentences. Experimental results show that the hybrid system produces a well-formed logical form with 88{\%} precision and 56{\%} recall.
Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths
Authors: Fei Cheng, Yusuke Miyao
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: General Classification, Question Answering, Relation, Relation Classification, Relation Extraction, Sentence, Temporal Relation Classification

Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP). We make a {``}common root{''} assumption to extend DP representations of cross-sentence links. In the final comparison to two state-of-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge, as well as manually annotated attributes of entities (class, tense, polarity, etc.).
Alternative Objective Functions for Training MT Evaluation Metrics
Authors: Milo{\v{s}} Stanojevi{\'c}, Khalil Sima{'}an
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Learning-To-Rank, Machine Translation, Sentence

MT evaluation metrics are tested for correlation with human judgments either at the sentence- or the corpus-level. Trained metrics ignore corpus-level judgments and are trained for high sentence-level correlation only. We show that training only for one objective (sentence or corpus level), can not only harm the performance on the other objective, but it can also be suboptimal for the objective being optimized. To this end we present a metric trained for corpus-level and show empirical comparison against a metric trained for sentence-level exemplifying how their performance may vary per language pair, type and level of judgment. Subsequently we propose a model trained to optimize both objectives simultaneously and show that it is far more stable than{--}and on average outperforms{--}both models on both objectives.
A Principled Framework for Evaluating Summarizers: Comparing Models of Summary Quality against Human Judgments
Authors: Maxime Peyrard, Judith Eckle-Kohler
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

We present a new framework for evaluating extractive summarizers, which is based on a principled representation as optimization problem. We prove that every extractive summarizer can be decomposed into an objective function and an optimization technique. We perform a comparative analysis and evaluation of several objective functions embedded in well-known summarizers regarding their correlation with human judgments. Our comparison of these correlations across two datasets yields surprising insights into the role and performance of objective functions in the different summarizers.
Vector space models for evaluating semantic fluency in autism
Authors: Emily Prud{'}hommeaux, Jan van Santen, Douglas Gliner
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

A common test administered during neurological examination is the semantic fluency test, in which the patient must list as many examples of a given semantic category as possible under timed conditions. Poor performance is associated with neurological conditions characterized by impairments in executive function, such as dementia, schizophrenia, and autism spectrum disorder (ASD). Methods for analyzing semantic fluency responses at the level of detail necessary to uncover these differences have typically relied on subjective manual annotation. In this paper, we explore automated approaches for scoring semantic fluency responses that leverage ontological resources and distributional semantic models to characterize the semantic fluency responses produced by young children with and without ASD. Using these methods, we find significant differences in the semantic fluency responses of children with ASD, demonstrating the utility of using objective methods for clinical language analysis.
Neural Architectures for Multilingual Semantic Parsing
Authors: Raymond Hendy Susanto, Wei Lu
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Machine Translation, Multi-Task Learning, Semantic Parsing

In this paper, we address semantic parsing in a multilingual context. We train one multilingual model that is capable of parsing natural language sentences from multiple different languages into their corresponding formal semantic representations. We extend an existing sequence-to-tree model to a multi-task learning framework which shares the decoder for generating semantic representations. We report evaluation results on the multilingual GeoQuery corpus and introduce a new multilingual version of the ATIS corpus.
Incorporating Uncertainty into Deep Learning for Spoken Language Assessment
Authors: Andrey Malinin, Anton Ragni, Kate Knill, Mark Gales
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Deep Learning

There is a growing demand for automatic assessment of spoken English proficiency. These systems need to handle large variations in input data owing to the wide range of candidate skill levels and L1s, and errors from ASR. Some candidates will be a poor match to the training data set, undermining the validity of the predicted grade. For high stakes tests it is essential for such systems not only to grade well, but also to provide a measure of their uncertainty in their predictions, enabling rejection to human graders. Previous work examined Gaussian Process (GP) graders which, though successful, do not scale well with large data sets. Deep Neural Network (DNN) may also be used to provide uncertainty using Monte-Carlo Dropout (MCD). This paper proposes a novel method to yield uncertainty and compares it to GPs and DNNs with MCD. The proposed approach explicitly teaches a DNN to have low uncertainty on training data and high uncertainty on generated artificial data. On experiments conducted on data from the Business Language Testing Service (BULATS), the proposed approach is found to outperform GPs and DNNs with MCD in uncertainty-based rejection whilst achieving comparable grading performance.
Incorporating Dialectal Variability for Socially Equitable Language Identification
Authors: David Jurgens, Yulia Tsvetkov, Dan Jurafsky
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Diversity, Language Identification

Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-to-sequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-the-art performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of {``}socially inclusive{''} NLP tools.
Evaluating Compound Splitters Extrinsically with Textual Entailment
Authors: Glorianna Jagfeld, Patrick Ziering, Lonneke van der Plas
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Information Retrieval, Machine Translation, Natural Language Inference, Speech Recognition, Translation

Traditionally, compound splitters are evaluated intrinsically on gold-standard data or extrinsically on the task of statistical machine translation. We explore a novel way for the extrinsic evaluation of compound splitters, namely recognizing textual entailment. Compound splitting has great potential for this novel task that is both transparent and well-defined. Moreover, we show that it addresses certain aspects that are either ignored in intrinsic evaluations or compensated for by taskinternal mechanisms in statistical machine translation. We show significant improvements using different compound splitting methods on a German textual entailment dataset.
On the Distribution of Lexical Features at Multiple Levels of Analysis
Authors: Fatemeh Almodaresi, Lyle Ungar, Vivek Kulkarni, Mohsen Zakeri, Salvatore Giorgi, H. Andrew Schwartz
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Document Classification, Sentiment Analysis

Natural language processing has increasingly moved from modeling documents and words toward studying the people behind the language. This move to working with data at the user or community level has presented the field with different characteristics of linguistic data. In this paper, we empirically characterize various lexical distributions at different levels of analysis, showing that, while most features are decidedly sparse and non-normal at the message-level (as with traditional NLP), they follow the central limit theorem to become much more Log-normal or even Normal at the user- and county-levels. Finally, we demonstrate that modeling lexical features for the correct level of analysis leads to marked improvements in common social scientific prediction tasks.
Exploring Neural Text Simplification Models
Authors: Sergiu Nisioi, Sanja {\v{S}}tajner, Simone Paolo Ponzetto, Liviu P. Dinu
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Lexical Simplification, Machine Translation, Text Simplification, Word Embeddings

We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems
On the Challenges of Translating NLP Research into Commercial Products
Authors: Daniel Dahlmeier
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

This paper highlights challenges in industrial research related to translating research in natural language processing into commercial products. While the interest in natural language processing from industry is significant, the transfer of research to commercial products is non-trivial and its challenges are often unknown to or underestimated by many researchers. I discuss current obstacles and provide suggestions for increasing the chances for translating research to commercial success based on my experience in industrial research.
Sentence Alignment Methods for Improving Text Simplification Systems
Authors: Sanja {\v{S}}tajner, Marc Franco-Salvador, Simone Paolo Ponzetto, Paolo Rosso, Heiner Stuckenschmidt
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Lexical Simplification, Machine Translation, Sentence, Text Simplification, Translation, Word Embeddings

We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.
Hybrid Neural Network Alignment and Lexicon Model in Direct HMM for Statistical Machine Translation
Authors: Weiyue Wang, Tamer Alkhouli, Derui Zhu, Hermann Ney
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, Translation, Word Alignment

Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct HMM with neural network-based lexicon and alignment models, which are trained jointly using the Baum-Welch algorithm. The direct HMM is applied to rerank the n-best list created by a state-of-the-art phrase-based translation system and it provides improvements by up to 1.0{\%} Bleu scores on two different translation tasks.
Information-Theory Interpretation of the Skip-Gram Negative-Sampling Objective Function
Authors: Oren Melamud, Jacob Goldberger
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing, Entity Extraction using GAN, Word Embeddings

In this paper we define a measure of dependency between two random variables, based on the Jensen-Shannon (JS) divergence between their joint distribution and the product of their marginal distributions. Then, we show that word2vec{'}s skip-gram with negative sampling embedding algorithm finds the optimal low-dimensional approximation of this JS dependency measure between the words and their contexts. The gap between the optimal score and the low-dimensional approximation is demonstrated on a standard text corpus.
Implicitly-Defined Neural Networks for Sequence Labeling
Authors: Michaeel Kazi, Brian Thompson
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Part-Of-Speech Tagging

In this work, we propose a novel, implicitly-defined neural network architecture and describe a method to compute its components. The proposed architecture forgoes the causality assumption used to formulate recurrent neural networks and instead couples the hidden states of the network, allowing improvement on problems with complex, long-distance dependencies. Initial experiments demonstrate the new architecture outperforms both the Stanford Parser and baseline bidirectional networks on the Penn Treebank Part-of-Speech tagging task and a baseline bidirectional network on an additional artificial random biased walk task.
The Role of Prosody and Speech Register in Word Segmentation: A Computational Modelling Perspective
Authors: Bogdan Ludusan, Reiko Mazuka, Mathieu Bernard, Alej Cristia, rina, Emmanuel Dupoux
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Language Acquisition, Segmentation

This study explores the role of speech register and prosody for the task of word segmentation. Since these two factors are thought to play an important role in early language acquisition, we aim to quantify their contribution for this task. We study a Japanese corpus containing both infant- and adult-directed speech and we apply four different word segmentation models, with and without knowledge of prosodic boundaries. The results showed that the difference between registers is smaller than previously reported and that prosodic boundary information helps more adult- than infant-directed speech.