pwc-archive/papers-with-abstracts huggingface dataset:
A Constituent-Centric Neural Architecture for Reading Comprehension
Authors: Pengtao Xie, Eric Xing
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension, Representation Learning

Reading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task. In this paper, we study the RC problem on the Stanford Question Answering Dataset (SQuAD). Observing from the training set that most correct answers are centered around constituents in the parse tree, we design a constituent-centric neural architecture where the generation of candidate answers and their representation learning are both based on constituents and guided by the parse tree. Under this architecture, the search space of candidate answers can be greatly reduced without sacrificing the coverage of correct answers and the syntactic, hierarchical and compositional structure among constituents can be well captured, which contributes to better representation learning of the candidate answers. On SQuAD, our method achieves the state of the art performance and the ablation study corroborates the effectiveness of individual modules.
Understanding and Predicting Empathic Behavior in Counseling Therapy
Authors: Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

Counselor empathy is associated with better outcomes in psychology and behavioral counseling. In this paper, we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy during motivational interviewing encounters. Particularly, we analyze aspects such as participants{'} engagement, participants{'} verbal and nonverbal accommodation, as well as topics being discussed during the conversation, with the final goal of identifying linguistic and acoustic markers of counselor empathy. We also show how we can use these findings alongside other raw linguistic and acoustic features to build accurate counselor empathy classifiers with accuracies of up to 80{\%}.
Prerequisite Relation Learning for Concepts in MOOCs
Authors: Liangming Pan, Chengjiang Li, Juanzi Li, Jie Tang
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Relation, Representation Learning

What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study the extent to which the prerequisite relation between knowledge concepts in Massive Open Online Courses (MOOCs) can be inferred automatically. In particular, what kinds of information can be leverage to uncover the potential prerequisite relation between knowledge concepts. We first propose a representation learning-based method for learning latent representations of course concepts, and then investigate how different features capture the prerequisite relations between concepts. Our experiments on three datasets form Coursera show that the proposed method achieves significant improvements (+5.9-48.0{\%} by F1-score) comparing with existing methods.
Unsupervised Text Segmentation Based on Native Language Characteristics
Authors: Shervin Malmasi, Mark Dras, Mark Johnson, Lan Du, Magdalena Wolska
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Segmentation, Text Segmentation

Most work on segmenting text does so on the basis of topic changes, but it can be of interest to segment by other, stylistically expressed characteristics such as change of authorship or native language. We propose a Bayesian unsupervised text segmentation approach to the latter. While baseline models achieve essentially random segmentation on our task, indicating its difficulty, a Bayesian model that incorporates appropriately compact language models and alternating asymmetric priors can achieve scores on the standard metrics around halfway to perfect segmentation.
Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks
Authors: Abhisek Chakrabarty, P, Onkar Arun it, Utpal Garain
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Attribute, LEMMA, Lemmatization, Vocal Bursts Valence Prediction

We introduce a composite deep neural network architecture for supervised and language independent context sensitive lemmatization. The proposed method considers the task as to identify the correct edit tree representing the transformation between a word-lemma pair. To find the lemma of a surface word, we exploit two successive bidirectional gated recurrent structures - the first one is used to extract the character level dependencies and the next one captures the contextual information of the given word. The key advantages of our model compared to the state-of-the-art lemmatizers such as Lemming and Morfette are - (i) it is independent of human decided features (ii) except the gold lemma, no other expensive morphological attribute is required for joint learning. We evaluate the lemmatizer on nine languages - Bengali, Catalan, Dutch, Hindi, Hungarian, Italian, Latin, Romanian and Spanish. It is found that except Bengali, the proposed method outperforms Lemming and Morfette on the other languages. To train the model on Bengali, we develop a gold lemma annotated dataset (having 1,702 sentences with a total of 20,257 word tokens), which is an additional contribution of this work.
Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation
Authors: Jinchao Zhang, Mingxuan Wang, Qun Liu, Jie zhou
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, NMT, Translation, Word Alignment

This paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance. Our proposed models enable attention mechanism to attend to source words regarding both the semantic requirement and the word reordering penalty. Experiments on Chinese-English translation show that the approaches can improve word alignment quality and achieve significant translation improvements over a basic attention-based NMT by large margins. Compared with previous works on identical corpora, our system achieves the state-of-the-art performance on translation quality.
Combating Human Trafficking with Multimodal Deep Models
Authors: Edmund Tong, Amir Zadeh, Cara Jones, Louis-Philippe Morency
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7

Human trafficking is a global epidemic affecting millions of people across the planet. Sex trafficking, the dominant form of human trafficking, has seen a significant rise mostly due to the abundance of escort websites, where human traffickers can openly advertise among at-will escort advertisements. In this paper, we take a major step in the automatic detection of advertisements suspected to pertain to human trafficking. We present a novel dataset called Trafficking-10k, with more than 10,000 advertisements annotated for this task. The dataset contains two sources of information per advertisement: text and images. For the accurate detection of trafficking advertisements, we designed and trained a deep multimodal model called the Human Trafficking Deep Network (HTDN).
MalwareTextDB: A Database for Annotated Malware Articles
Authors: Swee Kiat Lim, Aldrian Obaja Muis, Wei Lu, Chen Hui Ong
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Articles

Cybersecurity risks and malware threats are becoming increasingly dangerous and common. Despite the severity of the problem, there has been few NLP efforts focused on tackling cybersecurity. In this paper, we discuss the construction of a new database for annotated malware texts. An annotation framework is introduced based on the MAEC vocabulary for defining malware characteristics, along with a database consisting of 39 annotated APT reports with a total of 6,819 sentences. We also use the database to construct models that can potentially help cybersecurity researchers in their data collection and analytics efforts.
A Corpus of Annotated Revisions for Studying Argumentative Writing
Authors: Fan Zhang, Homa B. Hashemi, Rebecca Hwa, Diane Litman
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Argument Mining, Sentence

This paper presents ArgRewrite, a corpus of between-draft revisions of argumentative essays. Drafts are manually aligned at the sentence level, and the writer{'}s purpose for each revision is annotated with categories analogous to those used in argument mining and discourse analysis. The corpus should enable advanced research in writing comparison and revision analysis, as demonstrated via our own studies of student revision behavior and of automatic revision purpose prediction.
Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis
Authors: Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentence

The performance of Japanese predicate argument structure (PAS) analysis has improved in recent years thanks to the joint modeling of interactions between multiple predicates. However, this approach relies heavily on syntactic information predicted by parsers, and suffers from errorpropagation. To remedy this problem, we introduce a model that uses grid-type recurrent neural networks. The proposed model automatically induces features sensitive to multi-predicate interactions from the word sequence information of a sentence. Experiments on the NAIST Text Corpus demonstrate that without syntactic information, our model outperforms previous syntax-dependent models.
Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding
Authors: Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, Juanzi Li
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Entity Linking, Knowledge Graph Completion, Language Modeling, Language Modelling, Relation Extraction, Word Sense Disambiguation

Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative and quantitative analysis demonstrate the high quality of the word, entity and multi-prototype mention embeddings. Using entity linking as a study case, we apply our disambiguation method as well as the multi-prototype mention embeddings on the benchmark dataset, and achieve state-of-the-art performance.
Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication
Authors: Lanbo She, Joyce Chai
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Reinforcement Learning

To enable human-robot communication and collaboration, previous works represent grounded verb semantics as the potential change of state to the physical world caused by these verbs. Grounded verb semantics are acquired mainly based on the parallel data of the use of a verb phrase and its corresponding sequences of primitive actions demonstrated by humans. The rich interaction between teachers and students that is considered important in learning new skills has not yet been explored. To address this limitation, this paper presents a new interactive learning approach that allows robots to proactively engage in interaction with human partners by asking good questions to learn models for grounded verb semantics. The proposed approach uses reinforcement learning to allow the robot to acquire an optimal policy for its question-asking behaviors by maximizing the long-term reward. Our empirical results have shown that the interactive learning approach leads to more reliable models for grounded verb semantics, especially in the noisy environment which is full of uncertainties. Compared to previous work, the models acquired from interactive learning result in a 48{\%} to 145{\%} performance gain when applied in new situations.
Linguistic analysis of differences in portrayal of movie characters
Authors: Anil Ramakrishna, Victor R. Mart{\'\i}nez, Mal, Nikolaos rakis, Karan Singla, Shrikanth Narayanan
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: regression

We examine differences in portrayal of characters in movies using psycholinguistic and graph theoretic measures computed directly from screenplays. Differences are examined with respect to characters{'} gender, race, age and other metadata. Psycholinguistic metrics are extrapolated to dialogues in movies using a linear regression model built on a set of manually annotated seed words. Interesting patterns are revealed about relationships between genders of production team and the gender ratio of characters. Several correlations are noted between gender, race, age of characters and the linguistic metrics.
Linguistically Regularized LSTM for Sentiment Classification
Authors: Qiao Qian, Minlie Huang, Jinhao Lei, Xiaoyan Zhu
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Classification, General Classification, Negation, Sentence, Sentiment Analysis, Sentiment Classification

This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed recently, however, previous models either depend on expensive phrase-level annotation, most of which has remarkably degraded performance when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words). In this paper, we propose simple models trained with sentence-level annotation, but also attempt to model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are able to capture the linguistic role of sentiment words, negation words, and intensity words in sentiment expression.
Active Sentiment Domain Adaptation
Authors: Fangzhao Wu, Yongfeng Huang, Jun Yan
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Active Learning, Domain Adaptation, Sentiment Analysis, Transfer Learning

Domain adaptation is an important technology to handle domain dependence problem in sentiment analysis field. Existing methods usually rely on sentiment classifiers trained in source domains. However, their performance may heavily decline if the distributions of sentiment features in source and target domains have significant difference. In this paper, we propose an active sentiment domain adaptation approach to handle this problem. Instead of the source domain sentiment classifiers, our approach adapts the general-purpose sentiment lexicons to target domain with the help of a small number of labeled samples which are selected and annotated in an active learning mode, as well as the domain-specific sentiment similarities among words mined from unlabeled samples of target domain. A unified model is proposed to fuse different types of sentiment information and train sentiment classifier for target domain. Extensive experiments on benchmark datasets show that our approach can train accurate sentiment classifier with less labeled samples.
CANE: Context-Aware Network Embedding for Relation Modeling
Authors: Cunchao Tu, Han Liu, Zhiyuan Liu, Maosong Sun
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Community Detection, Link Prediction, Machine Translation, Network Embedding, Relation, Representation Learning

Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors. However, existing NE models aim to learn a fixed context-free embedding for each vertex and neglect the diverse roles when interacting with other vertices. In this paper, we assume that one vertex usually shows different aspects when interacting with different neighbor vertices, and should own different embeddings respectively. Therefore, we present Context-Aware Network Embedding (CANE), a novel NE model to address this issue. CANE learns context-aware embeddings for vertices with mutual attention mechanism and is expected to model the semantic relationships between vertices more precisely. In experiments, we compare our model with existing NE models on three real-world datasets. Experimental results show that CANE achieves significant improvement than state-of-the-art methods on link prediction and comparable performance on vertex classification. The source code and datasets can be obtained from \url{https://github.com/thunlp/CANE}.
Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing
Authors: Anssi Yli-Jyr{\"a}, Carlos G{\'o}mez-Rodr{\'\i}guez
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing, Semantic Parsing

We present a simple encoding for unlabeled noncrossing graphs and show how its latent counterpart helps us to represent several families of directed and undirected graphs used in syntactic and semantic parsing of natural language as context-free languages. The families are separated purely on the basis of forbidden patterns in latent encoding, eliminating the need to differentiate the families of non-crossing graphs in inference algorithms: one algorithm works for all when the search space can be controlled in parser input.
Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms
Authors: Shulin Liu, Yubo Chen, Kang Liu, Jun Zhao
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Event Detection

This paper tackles the task of event detection (ED), which involves identifying and categorizing events. We argue that arguments provide significant clues to this task, but they are either completely ignored or exploited in an indirect manner in existing detection approaches. In this work, we propose to exploit argument information explicitly for ED via supervised attention mechanisms. In specific, we systematically investigate the proposed model under the supervision of different attention strategies. Experimental results show that our approach advances state-of-the-arts and achieves the best F1 score on ACE 2005 dataset.
Topical Coherence in LDA-based Models through Induced Segmentation
Authors: Hesam Amoualian, Wei Lu, Eric Gaussier, Georgios Balikas, Massih R. Amini, Marianne Clausel
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Ad-Hoc Information Retrieval, General Classification, Information Retrieval, text-classification, Text Classification, Topic Models

This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.
Search-based Neural Structured Learning for Sequential Question Answering
Authors: Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Question Answering, Semantic Parsing

Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.
Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering
Authors: Jianbo Ye, Yan-ran Li, Zhaohui Wu, James Z. Wang, Wenjie Li, Jia Li
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Clustering, Word Embeddings

Word embeddings have become widely-used in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words. In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clustering empirical distributions. By using the Wasserstein distance between distributions, the word-to-word semantic relationship is taken into account in a principled way. The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets. More importantly, the method provides an effective framework for determining when and how much word embeddings contribute to document analysis. Experimental results with multiple embedding models are reported.
An Algebra for Feature Extraction
Authors: Vivek Srikumar
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Chunking, Relation Extraction

Though feature extraction is a necessary first step in statistical NLP, it is often seen as a mere preprocessing step. Yet, it can dominate computation time, both during training, and especially at deployment. In this paper, we formalize feature extraction from an algebraic perspective. Our formalization allows us to define a message passing algorithm that can restructure feature templates to be more computationally efficient. We show via experiments on text chunking and relation extraction that this restructuring does indeed speed up feature extraction in practice by reducing redundant computation.
Chunk-based Decoder for Neural Machine Translation
Authors: Shonosuke Ishiwatari, JingTao Yao, Shujie Liu, Mu Li, Ming Zhou, Naoki Yoshinaga, Masaru Kitsuregawa, Weijia Jia
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for (NMT), each of which consists of a chunk-level decoder and a word-level decoder. The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the word-level decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT {`}16 English-to-Japanese translation task.