pwc-archive/papers-with-abstracts huggingface dataset:
Using Context Information for Dialog Act Classification in DNN Framework
Authors: Yang Liu, Kun Han, Zhao Tan, Yun Lei
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Classification, Dialog Act Classification, General Classification, Sentence, Sentence Classification

Previous work on dialog act (DA) classification has investigated different methods, such as hidden Markov models, maximum entropy, conditional random fields, graphical models, and support vector machines. A few recent studies explored using deep learning neural networks for DA classification, however, it is not clear yet what is the best method for using dialog context or DA sequential information, and how much gain it brings. This paper proposes several ways of using context information for DA classification, all in the deep learning framework. The baseline system classifies each utterance using the convolutional neural networks (CNN). Our proposed methods include using hierarchical models (recurrent neural networks (RNN) or CNN) for DA sequence tagging where the bottom layer takes the sentence CNN representation as input, concatenating predictions from the previous utterances with the CNN vector for classification, and performing sequence decoding based on the predictions from the sentence CNN model. We conduct thorough experiments and comparisons on the Switchboard corpus, demonstrate that incorporating context information significantly improves DA classification, and show that we achieve new state-of-the-art performance for this task.
Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences
Authors: Yohan Jo, Michael Yoder, Hyeju Jang, Carolyn Ros{\'e}
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling

We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.
Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems
Authors: Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, Rui Yan
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Short-Text Conversation

The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given question. We propose an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way. Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit. Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.
Affordable On-line Dialogue Policy Learning
Authors: Cheng Chang, Runzhe Yang, Lu Chen, Xiang Zhou, Kai Yu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dialogue Management

The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable.
All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media
Authors: Jasabanta Patro, Bidisha Samanta, Saurabh Singh, Abhipsa Basu, Prithwish Mukherjee, Monojit Choudhury, Animesh Mukherjee
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: All, Language Identification, TAG

n this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman{'}s correlation values, our methods perform more than two times better (∼ 0.62) in predicting the borrowing likeliness compared to the best performing baseline (∼ 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88{\%} of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.
Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction
Authors: Tao Ding, Warren K. Bickel, SHimei Pan
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain on a large scale, to maximize system performance, we explore different unsupervised feature learning methods to take advantage of a large amount of unsupervised social media data. We also demonstrate the benefit of using multi-view unsupervised feature learning to combine heterogeneous user information such as Facebook {``}likes{''} and {``}status updates{''} to enhance system performance. Based on our evaluation, our best models achieved 86{\%} AUC for predicting tobacco use, 81{\%} for alcohol use and 84{\%} for illicit drug use, all of which significantly outperformed existing methods. Our investigation has also uncovered interesting relations between a user{'}s social media behavior (e.g., word usage) and substance use.
Demographic-aware word associations
Authors: Aparna Garimella, Carmen Banea, Rada Mihalcea
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Information Retrieval, Keyword Extraction, Relation Extraction

Variations of word associations across different groups of people can provide insights into people{'}s psychologies and their world views. To capture these variations, we introduce the task of demographic-aware word associations. We build a new gold standard dataset consisting of word association responses for approximately 300 stimulus words, collected from more than 800 respondents of different gender (male/female) and from different locations (India/United States), and show that there are significant variations in the word associations made by these groups. We also introduce a new demographic-aware word association model based on a neural net skip-gram architecture, and show how computational methods for measuring word associations that specifically account for writer demographics can outperform generic methods that are agnostic to such information.
A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
Authors: Hao Cheng, Hao Fang, Mari Ostendorf
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Feature Engineering

We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums. The model links different context with related language factors in the embedding space, providing a way to interpret the factored embeddings. Evaluated on a community endorsement prediction task using a large collection of topic-varying Reddit discussions, the factored embeddings consistently achieve improvement over other text representations. Qualitative analysis shows that the model captures community style and topic, as well as response trigger patterns.
Dimensions of Interpersonal Relationships: Corpus and Experiments
Authors: Farzana Rashid, Eduardo Blanco
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Question Answering

This paper presents a corpus and experiments to determine dimensions of interpersonal relationships. We define a set of dimensions heavily inspired by work in social science. We create a corpus by retrieving pairs of people, and then annotating dimensions for their relationships. A corpus analysis shows that dimensions can be annotated reliably. Experimental results show that given a pair of people, values to dimensions can be assigned automatically.
Argument Mining on Twitter: Arguments, Facts and Sources
Authors: Mihai Dusmanu, Elena Cabrio, Serena Villata
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Argument Mining

Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media. In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification. In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification. We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.
Distinguishing Japanese Non-standard Usages from Standard Ones
Authors: Tatsuya Aoki, Ryohei Sasano, Hiroya Takamura, Manabu Okumura
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Word Embeddings

We focus on non-standard usages of common words on social media. In the context of social media, words sometimes have other usages that are totally different from their original. In this study, we attempt to distinguish non-standard usages on social media from standard ones in an unsupervised manner. Our basic idea is that non-standardness can be measured by the inconsistency between the expected meaning of the target word and the given context. For this purpose, we use context embeddings derived from word embeddings. Our experimental results show that the model leveraging the context embedding outperforms other methods and provide us with findings, for example, on how to construct context embeddings and which corpus to use.
Connotation Frames of Power and Agency in Modern Films
Authors: Maarten Sap, Marcella Cindy Prasettio, Ari Holtzman, Hannah Rashkin, Yejin Choi
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

The framing of an action influences how we perceive its actor. We introduce connotation frames of power and agency, a pragmatic formalism organized using frame semantic representations, to model how different levels of power and agency are implicitly projected on actors through their actions. We use the new power and agency frames to measure the subtle, but prevalent, gender bias in the portrayal of modern film characters and provide insights that deviate from the well-known Bechdel test. Our contributions include an extended lexicon of connotation frames along with a web interface that provides a comprehensive analysis through the lens of connotation frames.
Controlling Human Perception of Basic User Traits
Authors: Daniel Preo{\c{t}}iuc-Pietro, Ch, Sharath ra Guntuku, Lyle Ungar
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Text Generation, Text Summarization, Translation

Much of our online communication is text-mediated and, lately, more common with automated agents. Unlike interacting with humans, these agents currently do not tailor their language to the type of person they are communicating to. In this pilot study, we measure the extent to which human perception of basic user trait information {--} gender and age {--} is controllable through text. Using automatic models of gender and age prediction, we estimate which tweets posted by a user are more likely to mis-characterize his traits. We perform multiple controlled crowdsourcing experiments in which we show that we can reduce the human prediction accuracy of gender to almost random {--} an over 20{\%} drop in accuracy. Our experiments show that it is practically feasible for multiple applications such as text generation, text summarization or machine translation to be tailored to specific traits and perceived as such.
Topic Signatures in Political Campaign Speeches
Authors: Cl{\'e}ment Gautrais, Peggy Cellier, Ren{\'e} Quiniou, Alex Termier, re
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

Highlighting the recurrence of topics usage in candidates speeches is a key feature to identify the main ideas of each candidate during a political campaign. In this paper, we present a method combining standard topic modeling with signature mining for analyzing topic recurrence in speeches of Clinton and Trump during the 2016 American presidential campaign. The results show that the method extracts automatically the main ideas of each candidate and, in addition, provides information about the evolution of these topics during the campaign.
Assessing Objective Recommendation Quality through Political Forecasting
Authors: H. Andrew Schwartz, Masoud Rouhizadeh, Michael Bishop, Philip Tetlock, Barbara Mellers, Lyle Ungar
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Sentence, Sentiment Analysis

Recommendations are often rated for their subjective quality, but few researchers have studied comment quality in terms of objective utility. We explore recommendation quality assessment with respect to both subjective (i.e. users{'} ratings) and objective (i.e., did it influence? did it improve decisions?) metrics in a massive online geopolitical forecasting system, ultimately comparing linguistic characteristics of each quality metric. Using a variety of features, we predict all types of quality with better accuracy than the simple yet strong baseline of comment length. Looking at the most predictive content illustrates rater biases; for example, forecasters are subjectively biased in favor of comments mentioning business transactions or dealings as well as material things, even though such comments do not indeed prove any more useful objectively. Additionally, more complex sentence constructions, as evidenced by subordinate conjunctions, are characteristic of comments leading to objective improvements in forecasting.
Never Abandon Minorities: Exhaustive Extraction of Bursty Phrases on Microblogs Using Set Cover Problem
Authors: Masumi Shirakawa, Takahiro Hara, Takuya Maekawa
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

We propose a language-independent data-driven method to exhaustively extract bursty phrases of arbitrary forms (e.g., phrases other than simple noun phrases) from microblogs. The burst (i.e., the rapid increase of the occurrence) of a phrase causes the burst of overlapping N-grams including incomplete ones. In other words, bursty incomplete N-grams inevitably overlap bursty phrases. Thus, the proposed method performs the extraction of bursty phrases as the set cover problem in which all bursty N-grams are covered by a minimum set of bursty phrases. Experimental results using Japanese Twitter data showed that the proposed method outperformed word-based, noun phrase-based, and segmentation-based methods both in terms of accuracy and coverage.
Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision
Authors: Haoruo Peng, Ming-Wei Chang, Wen-tau Yih
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Semantic Parsing

Neural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural network-based framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.
The Impact of Modeling Overall Argumentation with Tree Kernels
Authors: Henning Wachsmuth, Giovanni Da San Martino, Dora Kiesel, Benno Stein
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, text-classification, Text Classification

Several approaches have been proposed to model either the explicit sequential structure of an argumentative text or its implicit hierarchical structure. So far, the adequacy of these models of overall argumentation remains unclear. This paper asks what type of structure is actually important to tackle downstream tasks in computational argumentation. We analyze patterns in the overall argumentation of texts from three corpora. Then, we adapt the idea of positional tree kernels in order to capture sequential and hierarchical argumentative structure together for the first time. In systematic experiments for three text classification tasks, we find strong evidence for the impact of both types of structure. Our results suggest that either of them is necessary while their combination may be beneficial.
Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks
Authors: Hadi Amiri, Timothy Miller, Guergana Savova
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation

We present a novel approach for training artificial neural networks. Our approach is inspired by broad evidence in psychology that shows human learners can learn efficiently and effectively by increasing intervals of time between subsequent reviews of previously learned materials (spaced repetition). We investigate the analogy between training neural models and findings in psychology about human memory model and develop an efficient and effective algorithm to train neural models. The core part of our algorithm is a cognitively-motivated scheduler according to which training instances and their {``}reviews{''} are spaced over time. Our algorithm uses only 34-50{\%} of data per epoch, is 2.9-4.8 times faster than standard training, and outperforms competing state-of-the-art baselines. Our code is available at \url{scholar.harvard.edu/hadi/RbF/}.
Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
Authors: Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng, Xuanjing Huang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Articles, Part-Of-Speech Tagging, Stock Prediction

In this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a lack of large scale labeled datasets for this domain. To tackle these challenges, we propose a novel neural network to make use of out-of-domain labeled data, unlabeled in-domain data, and labeled in-domain data. Inspired by adversarial neural networks, the proposed method tries to learn common features through adversarial discriminator. In addition, we hypothesize that domain-specific features of target domain should be preserved in some degree. Hence, the proposed method adopts a sequence-to-sequence autoencoder to perform this task. Experimental results on three different datasets show that our method achieves better performance than state-of-the-art methods.
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
Authors: Bofang Li, Tao Liu, Zhe Zhao, Buzhou Tang, Aleks Drozd, R, Anna Rogers, Xiaoyong Du
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Chunking, Learning Word Embeddings, Named Entity Recognition (NER), Open-Ended Question Answering, Part-Of-Speech Tagging, Text Classification, Word Embeddings

The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.
Does syntax help discourse segmentation? Not so much
Authors: Chlo{\'e} Braud, Oph{\'e}lie Lacroix, Anders S{\o}gaard
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Discourse Parsing, Discourse Segmentation, Segmentation, Sentence

Discourse segmentation is the first step in building discourse parsers. Most work on discourse segmentation does not scale to real-world discourse parsing across languages, for two reasons: (i) models rely on constituent trees, and (ii) experiments have relied on gold standard identification of sentence and token boundaries. We therefore investigate to what extent constituents can be replaced with universal dependencies, or left out completely, as well as how state-of-the-art segmenters fare in the absence of sentence boundaries. Our results show that dependency information is less useful than expected, but we provide a fully scalable, robust model that only relies on part-of-speech information, and show that it performs well across languages in the absence of any gold-standard annotation.
Deal or No Deal? End-to-End Learning of Negotiation Dialogues
Authors: Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, Dhruv Batra
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other{'}s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.