pwc-archive/papers-with-abstracts huggingface dataset:
A Two-Stage Parsing Method for Text-Level Discourse Analysis
Authors: Yizhong Wang, Sujian Li, Houfeng Wang
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing, Document Summarization, Relation, Sentence, Sentiment Analysis, Vocal Bursts Valence Prediction

Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance. In this paper, we propose that transition-based model is more appropriate for parsing the naked discourse tree (i.e., identifying span and nuclearity) due to data sparsity. At the same time, we argue that relation labeling can benefit from naked tree structure and should be treated elaborately with consideration of three kinds of relations including within-sentence, across-sentence and across-paragraph relations. Thus, we design a pipelined two-stage parsing method for generating an RST tree from text. Experimental results show that our method achieves state-of-the-art performance, especially on span and nuclearity identification.
Error-repair Dependency Parsing for Ungrammatical Texts
Authors: Keisuke Sakaguchi, Matt Post, Benjamin Van Durme
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing, Sentence

We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.
Attention Strategies for Multi-Source Sequence-to-Sequence Learning
Authors: Jind{\v{r}}ich Libovick{\'y}, Jind{\v{r}}ich Helcl
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Automatic Post-Editing, Image Captioning, Machine Translation, Text Summarization, Translation

Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.
A Corpus of Natural Language for Visual Reasoning
Authors: Alane Suhr, Mike Lewis, James Yeh, Yoav Artzi
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Question Answering, Visual Question Answering (VQA), Visual Reasoning

We present a new visual reasoning language dataset, containing 92,244 pairs of examples of natural statements grounded in synthetic images with 3,962 unique sentences. We describe a method of crowdsourcing linguistically-diverse data, and present an analysis of our data. The data demonstrates a broad set of linguistic phenomena, requiring visual and set-theoretic reasoning. We experiment with various models, and show the data presents a strong challenge for future research.
Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers
Authors: Julien Tourille, Olivier Ferret, Aur{\'e}lie N{\'e}v{\'e}ol, Xavier Tannier
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Relation, Relation Extraction, Temporal Information Extraction, Temporal Relation Extraction

We present a neural architecture for containment relation identification between medical events and/or temporal expressions. We experiment on a corpus of de-identified clinical notes in English from the Mayo Clinic, namely the THYME corpus. Our model achieves an F-measure of 0.613 and outperforms the best result reported on this corpus to date.
How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models
Authors: Zhiliang Tian, Rui Yan, Lili Mou, Yiping Song, Yansong Feng, Dongyan Zhao
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

Generative conversational systems are attracting increasing attention in natural language processing (NLP). Recently, researchers have noticed the importance of context information in dialog processing, and built various models to utilize context. However, there is no systematic comparison to analyze how to use context effectively. In this paper, we conduct an empirical study to compare various models and investigate the effect of context information in dialog systems. We also propose a variant that explicitly weights context vectors by context-query relevance, outperforming the other baselines.
Cross-lingual and cross-domain discourse segmentation of entire documents
Authors: Chlo{\'e} Braud, Oph{\'e}lie Lacroix, Anders S{\o}gaard
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Discourse Segmentation, Segmentation, Sentence

Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parses and rich heuristics that are not generally available across languages and domains. In this paper, we propose statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations. We also consider the problem of learning discourse segmenters when no labeled data is available for a language. Our fully supervised system obtains 89.5{\%} F1 for English newswire, with slight drops in performance on other domains, and we report supervised and unsupervised (cross-lingual) results for five languages in total.
Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron?
Authors: Beata Beigman Klebanov, Binod Gyawali, Yi Song
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

Automatic identification of good arguments on a controversial topic has applications in civics and education, to name a few. While in the civics context it might be acceptable to create separate models for each topic, in the context of scoring of students{'} writing there is a preference for a single model that applies to all responses. Given that good arguments for one topic are likely to be irrelevant for another, is a single model for detecting good arguments a contradiction in terms? We investigate the extent to which it is possible to close the performance gap between topic-specific and across-topics models for identification of good arguments.
Argumentation Quality Assessment: Theory vs. Practice
Authors: Henning Wachsmuth, Nona Naderi, Ivan Habernal, Yufang Hou, Graeme Hirst, Iryna Gurevych, Benno Stein
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Argument Mining

Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches. This paper studies to what extent the views match empirically. We find that most observations on quality phrased spontaneously are in fact adequately represented by theory. Even more, relative comparisons of arguments in practice correlate with absolute quality ratings based on theory. Our results clarify how the two views can learn from each other.
Discourse Annotation of Non-native Spontaneous Spoken Responses Using the Rhetorical Structure Theory Framework
Authors: Xinhao Wang, James Bruno, Hillary Molloy, Keelan Evanini, Klaus Zechner
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, Text Generation, Text Summarization

The availability of the Rhetorical Structure Theory (RST) Discourse Treebank has spurred substantial research into discourse analysis of written texts; however, limited research has been conducted to date on RST annotation and parsing of spoken language, in particular, non-native spontaneous speech. Considering that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we initiated a research effort to obtain RST annotations of a large number of non-native spoken responses from a standardized assessment of academic English proficiency. The resulting inter-annotator kappa agreements on the three different levels of Span, Nuclearity, and Relation are 0.848, 0.766, and 0.653, respectively. Furthermore, a set of features was explored to evaluate the discourse structure of non-native spontaneous speech based on these annotations; the highest performing feature resulted in a correlation of 0.612 with scores of discourse coherence provided by expert human raters.
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings
Authors: Changxing Wu, Xiaodong Shi, Yidong Chen, Jinsong Su, Boli Wang
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: General Classification, Machine Translation, Question Answering, Relation, Word Embeddings

We introduce a simple and effective method to learn discourse-specific word embeddings (DSWE) for implicit discourse relation recognition. Specifically, DSWE is learned by performing connective classification on massive explicit discourse data, and capable of capturing discourse relationships between words. On the PDTB data set, using DSWE as features achieves significant improvements over baselines.
Oracle Summaries of Compressive Summarization
Authors: Tsutomu Hirao, Masaaki Nishino, Masaaki Nagata
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentence Compression

This paper derives an Integer Linear Programming (ILP) formulation to obtain an oracle summary of the compressive summarization paradigm in terms of ROUGE. The oracle summary is essential to reveal the upper bound performance of the paradigm. Experimental results on the DUC dataset showed that ROUGE scores of compressive oracles are significantly higher than those of extractive oracles and state-of-the-art summarization systems. These results reveal that compressive summarization is a promising paradigm and encourage us to continue with the research to produce informative summaries.
Japanese Sentence Compression with a Large Training Dataset
Authors: Shun Hasegawa, Yuta Kikuchi, Hiroya Takamura, Manabu Okumura
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentence, Sentence Compression

In English, high-quality sentence compression models by deleting words have been trained on automatically created large training datasets. We work on Japanese sentence compression by a similar approach. To create a large Japanese training dataset, a method of creating English training dataset is modified based on the characteristics of the Japanese language. The created dataset is used to train Japanese sentence compression models based on the recurrent neural network.
English Event Detection With Translated Language Features
Authors: Sam Wei, Igor Korostil, Joel Nothman, Ben Hachey
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Event Detection, Event Extraction, Named Entity Recognition (NER), Sentiment Analysis, Translation, Word Sense Disambiguation

We propose novel radical features from automatic translation for event extraction. Event detection is a complex language processing task for which it is expensive to collect training data, making generalisation challenging. We derive meaningful subword features from automatic translations into target language. Results suggest this method is particularly useful when using languages with writing systems that facilitate easy decomposition into subword features, e.g., logograms and Cangjie. The best result combines logogram features from Chinese and Japanese with syllable features from Korean, providing an additional 3.0 points f-score when added to state-of-the-art generalisation features on the TAC KBP 2015 Event Nugget task.
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering
Authors: Denis Savenkov, Eugene Agichtein
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Answer Selection, Feature Engineering, Information Retrieval, Knowledge Base Question Answering, Question Answering, Semantic Similarity, Semantic Textual Similarity

A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate. This paper proposes EviNets: a novel neural network architecture for factoid question answering. EviNets scores candidate answer entities by combining the available supporting evidence, e.g., structured knowledge bases and unstructured text documents. EviNets represents each piece of evidence with a dense embeddings vector, scores their relevance to the question, and aggregates the support for each candidate to predict their final scores. Each of the components is generic and allows plugging in a variety of models for semantic similarity scoring and information aggregation. We demonstrate the effectiveness of EviNets in experiments on the existing TREC QA and WikiMovies benchmarks, and on the new Yahoo! Answers dataset introduced in this paper. EviNets can be extended to other information types and could facilitate future work on combining evidence signals for joint reasoning in question answering.
Pocket Knowledge Base Population
Authors: Travis Wolfe, Mark Dredze, Benjamin Van Durme
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Knowledge Base Population, Open Information Extraction, Slot Filling

Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities. We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well anew annotations collected for this work. Our methods produce high quality KB from just text with many more entities and relationships than existing KBP systems.
Bootstrapping for Numerical Open IE
Authors: Swarnadeep Saha, Harinder Pal, {Mausam}
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Implicit Relations, Open Information Extraction, Sentence

We design and release BONIE, the first open numerical relation extractor, for extracting Open IE tuples where one of the arguments is a number or a quantity-unit phrase. BONIE uses bootstrapping to learn the specific dependency patterns that express numerical relations in a sentence. BONIE{'}s novelty lies in task-specific customizations, such as inferring implicit relations, which are clear due to context such as units (for e.g., {`}square kilometers{'} suggests area, even if the word {`}area{'} is missing in the sentence). BONIE obtains 1.5x yield and 15 point precision gain on numerical facts over a state-of-the-art Open IE system.
Feature-Rich Networks for Knowledge Base Completion
Authors: Alex Komninos, ros, Man, Suresh har
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Entity Linking, Knowledge Base Completion, Named Entity Recognition (NER), Question Answering, Relation Extraction

We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed models can better incorporate side information than previously proposed combinations of bilinear models with convolutional neural networks, showing large improvements when scoring the plausibility of unobserved facts with associated textual mentions.
Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets
Authors: Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, Iryna Gurevych
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Knowledge Base Population, Prediction, Question Answering, Sentence

Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. We show that this model outperforms previous methods on all three datasets. We make both the unified factuality corpus and our new model publicly available.
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
Authors: Chenhui Chu, Raj Dabre, Sadao Kurohashi
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

In this paper, we propose a novel domain adaptation method named {``}mixed fine tuning{''} for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.
Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings
Authors: Benjamin Marie, Atsushi Fujita
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Domain Adaptation, Information Retrieval, Machine Translation, Sentence, Translation, Word Embeddings

We propose a new method for extracting pseudo-parallel sentences from a pair of large monolingual corpora, without relying on any document-level information. Our method first exploits word embeddings in order to efficiently evaluate trillions of candidate sentence pairs and then a classifier to find the most reliable ones. We report significant improvements in domain adaptation for statistical machine translation when using a translation model trained on the sentence pairs extracted from in-domain monolingual corpora.
Feature Hashing for Language and Dialect Identification
Authors: Shervin Malmasi, Mark Dras
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Dialect Identification, Dimensionality Reduction, Information Retrieval, Language Identification, Machine Translation, Text Categorization

We evaluate feature hashing for language identification (LID), a method not previously used for this task. Using a standard dataset, we first show that while feature performance is high, LID data is highly dimensional and mostly sparse ({\textgreater}99.5{\%}) as it includes large vocabularies for many languages; memory requirements grow as languages are added. Next we apply hashing using various hash sizes, demonstrating that there is no performance loss with dimensionality reductions of up to 86{\%}. We also show that using an ensemble of low-dimension hash-based classifiers further boosts performance. Feature hashing is highly useful for LID and holds great promise for future work in this area.
Detection of Chinese Word Usage Errors for Non-Native Chinese Learners with Bidirectional LSTM
Authors: Yow-Ting Shiue, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Grammatical Error Detection, POS, Position, Sentence

Selecting appropriate words to compose a sentence is one common problem faced by non-native Chinese learners. In this paper, we propose (bidirectional) LSTM sequence labeling models and explore various features to detect word usage errors in Chinese sentences. By combining CWINDOW word embedding features and POS information, the best bidirectional LSTM model achieves accuracy 0.5138 and MRR 0.6789 on the HSK dataset. For 80.79{\%} of the test data, the model ranks the ground-truth within the top two at position level.
``Liar, Liar Pants on Fire'': A New Benchmark Dataset for Fake News Detection
Authors: William Yang Wang
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Deception Detection, Fact Checking, Fake News Detection, Sentiment Analysis

Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.
English Multiword Expression-aware Dependency Parsing Including Named Entities
Authors: Akihiko Kato, Hiroyuki Shindo, Yuji Matsumoto
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing

Because syntactic structures and spans of multiword expressions (MWEs) are independently annotated in many English syntactic corpora, they are generally inconsistent with respect to one another, which is harmful to the implementation of an aggregate system. In this work, we construct a corpus that ensures consistency between dependency structures and MWEs, including named entities. Further, we explore models that predict both MWE-spans and an MWE-aware dependency structure. Experimental results show that our joint model using additional MWE-span features achieves an MWE recognition improvement of 1.35 points over a pipeline model.
Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings
Authors: Terrence Szymanski
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Articles, Diachronic Word Embeddings, Word Embeddings

This paper introduces the concept of temporal word analogies: pairs of words which occupy the same semantic space at different points in time. One well-known property of word embeddings is that they are able to effectively model traditional word analogies ({``}word $w_1$ is to word $w_2$ as word $w_3$ is to word $w_4${''}) through vector addition. Here, I show that temporal word analogies ({``}word $w_1$ at time $t_\alpha$ is like word $w_2$ at time $t_\beta${''}) can effectively be modeled with diachronic word embeddings, provided that the independent embedding spaces from each time period are appropriately transformed into a common vector space. When applied to a diachronic corpus of news articles, this method is able to identify temporal word analogies such as {``}Ronald Reagan in 1987 is like Bill Clinton in 1997{''}, or {``}Walkman in 1987 is like iPod in 2007{''}.