pwc-archive/papers-with-abstracts huggingface dataset:
Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules
Authors: Xiaoshi Zhong, Aixin Sun, Erik Cambria
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Information Retrieval

Extracting time expressions from free text is a fundamental task for many applications. We analyze the time expressions from four datasets and find that only a small group of words are used to express time information, and the words in time expressions demonstrate similar syntactic behaviour. Based on the findings, we propose a type-based approach, named SynTime, to recognize time expressions. Specifically, we define three main syntactic token types, namely time token, modifier, and numeral, to group time-related regular expressions over tokens. On the types we design general heuristic rules to recognize time expressions. In recognition, SynTime first identifies the time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a light-weight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text of different types and of different domains. Experiment on benchmark datasets and tweets data shows that SynTime outperforms state-of-the-art methods.
Learning bilingual word embeddings with (almost) no bilingual data
Authors: Mikel Artetxe, Gorka Labaka, Eneko Agirre
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Document Classification, Entity Linking, Machine Translation, Multilingual Word Embeddings, Part-Of-Speech Tagging, Self-Learning, Transfer Learning, Word Embeddings

Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods that use document-aligned corpora or bilingual dictionaries of a few thousand words instead. In this work, we further reduce the need of bilingual resources using a very simple self-learning approach that can be combined with any dictionary-based mapping technique. Our method exploits the structural similarity of embedding spaces, and works with as little bilingual evidence as a 25 word dictionary or even an automatically generated list of numerals, obtaining results comparable to those of systems that use richer resources.
Deep Semantic Role Labeling: What Works and What's Next
Authors: Luheng He, Kenton Lee, Mike Lewis, Luke Zettlemoyer
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Predicate Detection, Semantic Role Labeling

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on theCoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10{\%} relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results.
Joint CTC/attention decoding for end-to-end speech recognition
Authors: Takaaki Hori, Shinji Watanabe, John Hershey
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

End-to-end automatic speech recognition (ASR) has become a popular alternative to conventional DNN/HMM systems because it avoids the need for linguistic resources such as pronunciation dictionary, tokenization, and context-dependency trees, leading to a greatly simplified model-building process. There are two major types of end-to-end architectures for ASR: attention-based methods use an attention mechanism to perform alignment between acoustic frames and recognized symbols, and connectionist temporal classification (CTC), uses Markov assumptions to efficiently solve sequential problems by dynamic programming. This paper proposes joint decoding algorithm for end-to-end ASR with a hybrid CTC/attention architecture, which effectively utilizes both advantages in decoding. We have applied the proposed method to two ASR benchmarks (spontaneous Japanese and Mandarin Chinese), and showing the comparable performance to conventional state-of-the-art DNN/HMM ASR systems without linguistic resources.
Deep Pyramid Convolutional Neural Networks for Text Categorization
Authors: Rie Johnson, Tong Zhang
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentiment Analysis, Sentiment Classification, Text Classification

This paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that can efficiently represent long-range associations in text. In the literature, several deep and complex neural networks have been proposed for this task, assuming availability of relatively large amounts of training data. However, the associated computational complexity increases as the networks go deeper, which poses serious challenges in practical applications. Moreover, it was shown recently that shallow word-level CNNs are more accurate and much faster than the state-of-the-art very deep nets such as character-level CNNs even in the setting of large training data. Motivated by these findings, we carefully studied deepening of word-level CNNs to capture global representations of text, and found a simple network architecture with which the best accuracy can be obtained by increasing the network depth without increasing computational cost by much. We call it deep pyramid CNN. The proposed model with 15 weight layers outperforms the previous best models on six benchmark datasets for sentiment classification and topic categorization.
Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations
Authors: Gabriel Doyle, Amir Goldberg, Sameer Srivastava, Michael Frank
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Language Modeling, Language Modelling

Cultural fit is widely believed to affect the success of individuals and the groups to which they belong. Yet it remains an elusive, poorly measured construct. Recent research draws on computational linguistics to measure cultural fit but overlooks asymmetries in cultural adaptation. By contrast, we develop a directed, dynamic measure of cultural fit based on linguistic alignment, which estimates the influence of one person{'}s word use on another{'}s and distinguishes between two enculturation mechanisms: internalization and self-regulation. We use this measure to trace employees{'} enculturation trajectories over a large, multi-year corpus of corporate emails and find that patterns of alignment in the first six months of employment are predictive of individuals{'} downstream outcomes, especially involuntary exit. Further predictive analyses suggest referential alignment plays an overlooked role in linguistic alignment.
Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance
Authors: Yang Xu, David Reitter
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication. We predict the success of collaborative task in English and Danish corpora of task-oriented dialogue. Two features are extracted from the frequency domain representations of the lexical entropy series of each interlocutor, power spectrum overlap (PSO) and relative phase (RP). We find that PSO is a negative predictor of task success, while RP is a positive one. An SVM with these features significantly improved on previous task success prediction models. Our findings suggest that the strategic distribution of information density between interlocutors is relevant to task success.
Domain Attention with an Ensemble of Experts
Authors: Young-Bum Kim, Karl Stratos, Dongchan Kim
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Domain Adaptation, Spoken Language Understanding

An important problem in domain adaptation is to quickly generalize to a new domain with limited supervision given K existing domains. One approach is to retrain a global model across all K + 1 domains using standard techniques, for instance Daum{\'e} III (2009). However, it is desirable to adapt without having to re-estimate a global model from scratch each time a new domain with potentially new intents and slots is added. We describe a solution based on attending an ensemble of domain experts. We assume K domain specific intent and slot models trained on respective domains. When given domain K + 1, our model uses a weighted combination of the K domain experts{'} feedback along with its own opinion to make predictions on the new domain. In experiments, the model significantly outperforms baselines that do not use domain adaptation and also performs better than the full retraining approach.
Generating Contrastive Referring Expressions
Authors: Mart{\'\i}n Villalba, Christoph Teichmann, Alex Koller, er
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Text Generation

The referring expressions (REs) produced by a natural language generation (NLG) system can be misunderstood by the hearer, even when they are semantically correct. In an interactive setting, the NLG system can try to recognize such misunderstandings and correct them. We present an algorithm for generating corrective REs that use contrastive focus ({``}no, the BLUE button{''}) to emphasize the information the hearer most likely misunderstood. We show empirically that these contrastive REs are preferred over REs without contrast marking.
Sequence-to-Dependency Neural Machine Translation
Authors: Shuangzhi Wu, Dong-dong Zhang, Nan Yang, Mu Li, Ming Zhou
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, NMT, Translation

Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and Japanese-English translation tasks.
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
Authors: Jing Ma, Wei Gao, Kam-Fai Wong
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-of-the-art rumor detection models.
EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks
Authors: Muhammad Abdul-Mageed, Lyle Ungar
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Decision Making

Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58{\%}). We also extend the task beyond emotion types to model Robert Plutick{'}s 8 primary emotion dimensions, acquiring a superior accuracy of 95.68{\%}.
Beyond Binary Labels: Political Ideology Prediction of Twitter Users
Authors: Daniel Preo{\c{t}}iuc-Pietro, Ye Liu, Daniel Hopkins, Lyle Ungar
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Prediction

Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US. This study examines users{'} political ideology using a seven-point scale which enables us to identify politically moderate and neutral users {--} groups which are of particular interest to political scientists and pollsters. Using a novel data set with political ideology labels self-reported through surveys, our goal is two-fold: a) to characterize the groups of politically engaged users through language use on Twitter; b) to build a fine-grained model that predicts political ideology of unseen users. Our results identify differences in both political leaning and engagement and the extent to which each group tweets using political keywords. Finally, we demonstrate how to improve ideology prediction accuracy by exploiting the relationships between the user groups.
Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter
Authors: Kristen Johnson, Di Jin, Dan Goldwasser
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: General Classification

Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone.
TextFlow: A Text Similarity Measure based on Continuous Sequences
Authors: Yassine Mrabet, Halil Kilicoglu, Dina Demner-Fushman
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Natural Language Inference, Position, Text Generation, text similarity

Text similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment. While recent advances in deep learning highlighted the relevance of sequential models in natural language generation, existing similarity measures do not fully exploit the sequential nature of language. Examples of such similarity measures include n-grams and skip-grams overlap which rely on distinct slices of the input texts. In this paper we present a novel text similarity measure inspired from a common representation in DNA sequence alignment algorithms. The new measure, called TextFlow, represents input text pairs as continuous curves and uses both the actual position of the words and sequence matching to compute the similarity value. Our experiments on 8 different datasets show very encouraging results in paraphrase detection, textual entailment recognition and ranking relevance.
Polish evaluation dataset for compositional distributional semantics models
Authors: Alina Wr{\'o}blewska, Katarzyna Krasnowska-Kiera{\'s}
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Semantic Composition, Sentence

The paper presents a procedure of building an evaluation dataset. for the validation of compositional distributional semantics models estimated for languages other than English. The procedure generally builds on steps designed to assemble the SICK corpus, which contains pairs of English sentences annotated for semantic relatedness and entailment, because we aim at building a comparable dataset. However, the implementation of particular building steps significantly differs from the original SICK design assumptions, which is caused by both lack of necessary extraneous resources for an investigated language and the need for language-specific transformation rules. The designed procedure is verified on Polish, a fusional language with a relatively free word order, and contributes to building a Polish evaluation dataset. The resource consists of 10K sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish.
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
Authors: Christopher Bryant, Mariano Felice, Ted Briscoe
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Annotated Code Search, Grammatical Error Correction, News Annotation, Table annotation, Vocal Bursts Type Prediction

Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule-based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as {``}Good{''} or {``}Acceptable{''} in at least 95{\%} of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time.
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability
Authors: Saku Sugawara, Yusuke Kido, Hikaru Yokono, Akiko Aizawa
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Coreference Resolution, Machine Reading Comprehension, Natural Language Understanding, Reading Comprehension

Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest and SQuAD, and highlighted the characteristics of the datasets according to each metric and the correlation between the two classes. Our dataset analysis suggests that the readability of RC datasets does not directly affect the question difficulty and that it is possible to create an RC dataset that is easy to read but difficult to answer.
Semantic Dependency Parsing via Book Embedding
Authors: Weiwei Sun, Junjie Cao, Xiaojun Wan
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Combinatorial Optimization, Dependency Parsing, Semantic Dependency Parsing, Sentence

We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algorithm tocombine pages into a book. Experiments demonstrate the effectiveness of the bookembedding framework across a wide range of conditions. Our parser obtains comparable results with a state-of-the-art transition-based parser.
Context-Dependent Sentiment Analysis in User-Generated Videos
Authors: Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Navonil Majumder, Amir Zadeh, Louis-Philippe Morency
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Emotion Recognition, Emotion Recognition in Conversation, General Classification, Multimodal Emotion Recognition, Multimodal Sentiment Analysis, Named Entity Recognition (NER), Sarcasm Detection, Sentiment Analysis

Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10{\%} performance improvement over the state of the art and high robustness to generalizability.
A Multidimensional Lexicon for Interpersonal Stancetaking
Authors: Umashanthi Pavalanathan, Jim Fitzpatrick, Scott Kiesling, Jacob Eisenstein
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Word Embeddings

The sociolinguistic construct of stancetaking describes the activities through which discourse participants create and signal relationships to their interlocutors, to the topic of discussion, and to the talk itself. Stancetaking underlies a wide range of interactional phenomena, relating to formality, politeness, affect, and subjectivity. We present a computational approach to stancetaking, in which we build a theoretically-motivated lexicon of stance markers, and then use multidimensional analysis to identify a set of underlying stance dimensions. We validate these dimensions intrinscially and extrinsically, showing that they are internally coherent, match pre-registered hypotheses, and correlate with social phenomena.
Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling
Authors: Jeffrey Lund, Connor Cook, Kevin Seppi, Jordan Boyd-Graber
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Document Classification, Information Retrieval, Sentiment Analysis, Topic Models

Interactive topic models are powerful tools for those seeking to understand large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor methods, which use a single word to uniquely identify a topic, offer the speed needed for interactive work but lack both a mechanism to inject prior knowledge and lack the intuitive semantics needed for user-facing applications. We propose combinations of words as anchors, going beyond existing single word anchor algorithms{---}an approach we call {``}Tandem Anchors{''}. We begin with a synthetic investigation of this approach then apply the approach to interactive topic modeling in a user study and compare it to interactive and non-interactive approaches. Tandem anchors are faster and more intuitive than existing interactive approaches.