pwc-archive/papers-with-abstracts huggingface dataset:
A Genre-Aware Attention Model to Improve the Likability Prediction of Books
Authors: Suraj Maharjan, Manuel Montes, Fabio A. Gonz{\'a}lez, Thamar Solorio
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Feature Importance, Prediction

Likability prediction of books has many uses. Readers, writers, as well as the publishing industry, can all benefit from automatic book likability prediction systems. In order to make reliable decisions, these systems need to assimilate information from different aspects of a book in a sensible way. We propose a novel multimodal neural architecture that incorporates genre supervision to assign weights to individual feature types. Our proposed method is capable of dynamically tailoring weights given to feature types based on the characteristics of each book. Our architecture achieves competitive results and even outperforms state-of-the-art for this task.
Thread Popularity Prediction and Tracking with a Permutation-invariant Model
Authors: Hou Pong Chan, Irwin King
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Reinforcement Learning

The task of thread popularity prediction and tracking aims to recommend a few popular comments to subscribed users when a batch of new comments arrive in a discussion thread. This task has been formulated as a reinforcement learning problem, in which the reward of the agent is the sum of positive responses received by the recommended comments. In this work, we propose a novel approach to tackle this problem. First, we propose a deep neural network architecture to model the expected cumulative reward (Q-value) of a recommendation (action). Unlike the state-of-the-art approach, which treats an action as a sequence, our model uses an attention mechanism to integrate information from a set of comments. Thus, the prediction of Q-value is invariant to the permutation of the comments, which leads to a more consistent agent behavior. Second, we employ a greedy procedure to approximate the action that maximizes the predicted Q-value from a combinatorial action space. Different from the state-of-the-art approach, this procedure does not require an additional pre-trained model to generate candidate actions. Experiments on five real-world datasets show that our approach outperforms the state-of-the-art.
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
Authors: Navonil Majumder, Soujanya Poria, Alex Gelbukh, er, Md. Shad Akhtar, Erik Cambria, Asif Ekbal
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Extract Aspect, General Classification, Relation, Sentiment Analysis, Sentiment Classification

Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6{\%} on average in two distinct domains: restaurant and laptop.
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations
Authors: Zhe Zhang, Munindar Singh
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Diversity, General Classification, Semantic Similarity, Semantic Textual Similarity, Sentiment Analysis, Sentiment Classification, Topic Models, Word Embeddings

We propose Limbic, an unsupervised probabilistic model that addresses the problem of discovering aspects and sentiments and associating them with authors of opinionated texts. Limbic combines three ideas, incorporating authors, discourse relations, and word embeddings. For discourse relations, Limbic adopts a generative process regularized by a Markov Random Field. To promote words with high semantic similarity into the same topic, Limbic captures semantic regularities from word embeddings via a generalized P{\'o}lya Urn process. We demonstrate that Limbic (1) discovers aspects associated with sentiments with high lexical diversity; (2) outperforms state-of-the-art models by a substantial margin in topic cohesion and sentiment classification.
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking
Authors: Yang Yang, Deyu Zhou, Yulan He
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Common Sense Reasoning, Emotion Classification, Multi-Label Learning, Topic Models, Transfer Learning

Text might express or evoke multiple emotions with varying intensities. As such, it is crucial to predict and rank multiple relevant emotions by their intensities. Moreover, as emotions might be evoked by hidden topics, it is important to unveil and incorporate such topical information to understand how the emotions are evoked. We proposed a novel interpretable neural network approach for relevant emotion ranking. Specifically, motivated by transfer learning, the neural network is initialized to make the hidden layer approximate the behavior of topic models. Moreover, a novel error function is defined to optimize the whole neural network for relevant emotion ranking. Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label learning methods. Moreover, the extracted emotion-associated topic words indeed represent emotion-evoking events and are in line with our common-sense knowledge.
Multi-grained Attention Network for Aspect-Level Sentiment Classification
Authors: Feifan Fan, Yansong Feng, Dongyan Zhao
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentiment Analysis, Sentiment Classification

We propose a novel multi-grained attention network (MGAN) model for aspect level sentiment classification. Existing approaches mostly adopt coarse-grained attention mechanism, which may bring information loss if the aspect has multiple words or larger context. We propose a fine-grained attention mechanism, which can capture the word-level interaction between aspect and context. And then we leverage the fine-grained and coarse-grained attention mechanisms to compose the MGAN framework. Moreover, unlike previous works which train each aspect with its context separately, we design an aspect alignment loss to depict the aspect-level interactions among the aspects that have the same context. We evaluate the proposed approach on three datasets: laptop and restaurant are from SemEval 2014, and the last one is a twitter dataset. Experimental results show that the multi-grained attention network consistently outperforms the state-of-the-art methods on all three datasets. We also conduct experiments to evaluate the effectiveness of aspect alignment loss, which indicates the aspect-level interactions can bring extra useful information and further improve the performance.
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
Authors: Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, Opinion Mining, Representation Learning, Sentence, Sentiment Analysis, Sentiment Classification

This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources. The key idea is that that incorporating a word-level prior can aid in the representation learning process, eventually improving model performance. To this end, our model employs two distinctly unique components, i.e., (1) we introduce a lexicon-driven contextual attention mechanism to imbue lexicon words with long-range contextual information and (2), we introduce a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. Via extensive experiments, we show that our approach outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis
Authors: Deepanway Ghosal, Md. Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Multimodal Sentiment Analysis, Sentiment Analysis

Multi-modal sentiment analysis offers various challenges, one being the effective combination of different input modalities, namely text, visual and acoustic. In this paper, we propose a recurrent neural network based multi-modal attention framework that leverages the contextual information for utterance-level sentiment prediction. The proposed approach applies attention on multi-modal multi-utterance representations and tries to learn the contributing features amongst them. We evaluate our proposed approach on two multi-modal sentiment analysis benchmark datasets, viz. CMU Multi-modal Opinion-level Sentiment Intensity (CMU-MOSI) corpus and the recently released CMU Multi-modal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) corpus. Evaluation results show the effectiveness of our proposed approach with the accuracies of 82.31{\%} and 79.80{\%} for the MOSI and MOSEI datasets, respectively. These are approximately 2 and 1 points performance improvement over the state-of-the-art models for the datasets.
ExtRA: Extracting Prominent Review Aspects from Customer Feedback
Authors: Zhiyi Luo, Shanshan Huang, Frank F. Xu, Bill Yuchen Lin, Hanyuan Shi, Kenny Zhu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost every day. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.
Legal Judgment Prediction via Topological Learning
Authors: Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, Maosong Sun
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Multi-Task Learning, Prediction, Text Classification

Legal Judgment Prediction (LJP) aims to predict the judgment result based on the facts of a case and becomes a promising application of artificial intelligence techniques in the legal field. In real-world scenarios, legal judgment usually consists of multiple subtasks, such as the decisions of applicable law articles, charges, fines, and the term of penalty. Moreover, there exist topological dependencies among these subtasks. While most existing works only focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks, we formalize the dependencies among subtasks as a Directed Acyclic Graph (DAG) and propose a topological multi-task learning framework, TopJudge, which incorporates multiple subtasks and DAG dependencies into judgment prediction. We conduct experiments on several real-world large-scale datasets of criminal cases in the civil law system. Experimental results show that our model achieves consistent and significant improvements over baselines on all judgment prediction tasks. The source code can be obtained from \url{https://github.com/thunlp/TopJudge}.
Native Language Identification with User Generated Content
Authors: Gili Goldin, Ella Rabinovich, Shuly Wintner
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Identification, Native Language Identification

We address the task of native language identification in the context of social media content, where authors are highly-fluent, advanced nonnative speakers (of English). Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. We provide a detailed analysis of the features that sheds light on differences between native and nonnative speakers, and among nonnative speakers with different backgrounds.
Sentiment Classification towards Question-Answering with Hierarchical Matching Network
Authors: Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Opinion Mining, Question Answering, Sentence, Sentiment Analysis, Sentiment Classification

In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.
Cross-topic Argument Mining from Heterogeneous Sources
Authors: Christian Stab, Tristan Miller, Benjamin Schiller, Pranav Rai, Iryna Gurevych
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Argument Mining, Decision Making, Information Retrieval, Multi-Task Learning, Question Answering

Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches are designed for use only with specific text types and fall short when applied to heterogeneous texts. In this paper, we propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. We source annotations for over 25,000 instances covering eight controversial topics. We show that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs by more than 3 percentage points in F1 in two- and three-label cross-topic settings. We also show that these results can be further improved by leveraging additional data for topic relevance using multi-task learning.
CARER: Contextualized Affect Representations for Emotion Recognition
Authors: Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, Yi-Shin Chen
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Emotion Recognition, Semantic Textual Similarity, Word Embeddings

Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.
CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization
Authors: Semih Yavuz, Chung-Cheng Chiu, Patrick Nguyen, Yonghui Wu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization, Image Captioning, Machine Translation, Policy Gradient Methods, Structured Prediction, Text Generation, Text Summarization, Translation

Maximum-likelihood estimation (MLE) is one of the most widely used approaches for training structured prediction models for text-generation based natural language processing applications. However, besides exposure bias, models trained with MLE suffer from wrong objective problem where they are trained to maximize the word-level correct next step prediction, but are evaluated with respect to sequence-level discrete metrics such as ROUGE and BLEU. Several variants of policy-gradient methods address some of these problems by optimizing for final discrete evaluation metrics and showing improvements over MLE training for downstream tasks like text summarization and machine translation. However, policy-gradient methods suffers from high sample variance, making the training process very difficult and unstable. In this paper, we present an alternative direction towards mitigating this problem by introducing a new objective (CaLcs) based on a differentiable surrogate of longest common subsequence (LCS) measure that captures sequence-level structure similarity. Experimental results on abstractive summarization and machine translation validate the effectiveness of the proposed approach.
Phrase-level Self-Attention Networks for Universal Sentence Encoding
Authors: Wei Wu, Houfeng Wang, Tianyu Liu, Shuming Ma
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Multi-class Classification, Natural Language Inference, Sentence, Sentence Classification, Sentence Similarity, Word Embeddings

Universal sentence encoding is a hot topic in recent NLP research. Attention mechanism has been an integral part in many sentence encoding models, allowing the models to capture context dependencies regardless of the distance between the elements in the sequence. Fully attention-based models have recently attracted enormous interest due to their highly parallelizable computation and significantly less training time. However, the memory consumption of their models grows quadratically with the sentence length, and the syntactic information is neglected. To this end, we propose Phrase-level Self-Attention Networks (PSAN) that perform self-attention across words inside a phrase to capture context dependencies at the phrase level, and use the gated memory updating mechanism to refine each word{'}s representation hierarchically with longer-term context dependencies captured in a larger phrase. As a result, the memory consumption can be reduced because the self-attention is performed at the phrase level instead of the sentence level. At the same time, syntactic information can be easily integrated in the model. Experiment results show that PSAN can achieve the state-of-the-art performance across a plethora of NLP tasks including binary and multi-class classification, natural language inference and sentence similarity.
Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data
Authors: Guanghui Qin, Jin-Ge Yao, Xuening Wang, Jinpeng Wang, Chin-Yew Lin
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Grounded language learning, Language Acquisition, Text Generation

Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms. In this paper, we attempt at learning explicit latent semantic annotations from paired structured tables and texts, establishing correspondences between various types of values and texts. We model the joint probability of data fields, texts, phrasal spans, and latent annotations with an adapted semi-hidden Markov model, and impose a soft statistical constraint to further improve the performance. As a by-product, we leverage the induced annotations to extract templates for language generation. Experimental results suggest the feasibility of the setting in this study, as well as the effectiveness of our proposed framework.
A Teacher-Student Framework for Maintainable Dialog Manager
Authors: Weikang Wang, Jiajun Zhang, Han Zhang, Mei-Yuh Hwang, Cheng-qing Zong, Zhifei Li
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems. However, extending RL-based systems to handle new intents and slots requires a system redesign. The high maintenance cost makes it difficult to apply RL methods to practical systems on a large scale. To address this issue, we propose a practical teacher-student framework to extend RL-based dialog systems without retraining from scratch. Specifically, the {``}student{''} is an extended dialog manager based on a new ontology, and the {``}teacher{''} is existing resources used for guiding the learning process of the {``}student{''}. By specifying constraints held in the new dialog manager, we transfer knowledge of the {``}teacher{''} to the {``}student{''} without additional resources. Experiments show that the performance of the extended system is comparable to the system trained from scratch. More importantly, the proposed framework makes no assumption about the unsupported intents and slots, which makes it possible to improve RL-based systems incrementally.
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding
Authors: Changliang Li, Liang Li, Ji Qi
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Automatic Speech Recognition (ASR), Intent Detection, Language Modeling, Language Modelling, slot-filling, Slot Filling, Speech Recognition, Spoken Dialogue Systems, Spoken Language Understanding

Spoken Language Understanding (SLU), which typically involves intent determination and slot filling, is a core component of spoken dialogue systems. Joint learning has shown to be effective in SLU given that slot tags and intents are supposed to share knowledge with each other. However, most existing joint learning methods only consider joint learning by sharing parameters on surface level rather than semantic level. In this work, we propose a novel self-attentive model with gate mechanism to fully utilize the semantic correlation between slot and intent. Our model first obtains intent-augmented embeddings based on neural network with self-attention mechanism. And then the intent semantic representation is utilized as the gate for labelling slot tags. The objectives of both tasks are optimized simultaneously via joint learning in an end-to-end way. We conduct experiment on popular benchmark ATIS. The results show that our model achieves state-of-the-art and outperforms other popular methods by a large margin in terms of both intent detection error rate and slot filling F1-score. This paper gives a new perspective for research on SLU.