pwc-archive/papers-with-abstracts huggingface dataset:
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
Authors: Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Attribute, Entity Alignment, Feature Engineering, Knowledge Graphs, Machine Translation

Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which is an important way to enrich the cross-lingual links in multilingual KGs. In this paper, we propose a novel approach for cross-lingual KG alignment via graph convolutional networks (GCNs). Given a set of pre-aligned entities, our approach trains GCNs to embed entities of each language into a unified vector space. Entity alignments are discovered based on the distances between entities in the embedding space. Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments. In the experiments on aligning real multilingual KGs, our approach gets the best performance compared with other embedding-based KG alignment approaches.
Cross-lingual Lexical Sememe Prediction
Authors: Fanchao Qi, Yankai Lin, Maosong Sun, Hao Zhu, Ruobing Xie, Zhiyuan Liu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Learning Word Embeddings, Multilingual Word Embeddings, Prediction, Word Embeddings

Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe prediction, aiming to automatically predict sememes for words in other languages. We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. Experimental results on real-world datasets show that our proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingual sememe prediction. The codes and data of this paper are available at \url{https://github.com/thunlp/CL-SP}.
Addressing Troublesome Words in Neural Machine Translation
Authors: Yang Zhao, Jiajun Zhang, Zhongjun He, Cheng-qing Zong, Hua Wu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, NMT, Translation

One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words. To address this problem, we propose a novel memoryenhanced NMT method. First, we investigate different strategies to define and detect the troublesome words. Then, a contextual memory is constructed to memorize which target words should be produced in what situations. Finally, we design a hybrid model to dynamically access the contextual memory so as to correctly translate the troublesome words. The extensive experiments on Chinese-to-English and English-to-German translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling troublesome words.
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination
Authors: Jiali Zeng, Jinsong Su, Huating Wen, Yang Liu, Jun Xie, Yongjing Yin, Jianqiang Zhao
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: domain classification, Machine Translation, NMT, Sentence, Translation

With great practical value, the study of Multi-domain Neural Machine Translation (NMT) mainly focuses on using mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. Intuitively, words in a sentence are related to its domain to varying degrees, so that they will exert disparate impacts on the multi-domain NMT modeling. Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT. To this end, we jointly model NMT with monolingual attention-based domain classification tasks and improve NMT as follows: 1) Based on the sentence representations produced by a domain classifier and an adversarial domain classifier, we generate two gating vectors and use them to construct domain-specific and domain-shared annotations, for later translation predictions via different attention models; 2) We utilize the attention weights derived from target-side domain classifier to adjust the weights of target words in the training objective, enabling domain-related words to have greater impacts during model training. Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. Source codes of this paper are available on Github \url{https://github.com/DeepLearnXMU/WDCNMT}.
NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings
Authors: Ndapa Nakashole
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Multilingual Word Embeddings, Translation, Word Embeddings, Word Translation

Inducing multilingual word embeddings by learning a linear map between embedding spaces of different languages achieves remarkable accuracy on related languages. However, accuracy drops substantially when translating between distant languages. Given that languages exhibit differences in vocabulary, grammar, written form, or syntax, one would expect that embedding spaces of different languages have different structures especially for distant languages. With the goal of capturing such differences, we propose a method for learning neighborhood sensitive maps, NORMA. Our experiments show that NORMA outperforms current state-of-the-art methods for word translation between distant languages.
Adaptive Multi-pass Decoder for Neural Machine Translation
Authors: Xinwei Geng, Xiaocheng Feng, Bing Qin, Ting Liu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Machine Translation, NMT, Reinforcement Learning, Reinforcement Learning (RL), Translation

Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored. In this paper, we propose a novel architecture called adaptive multi-pass decoder, which introduces a flexible multi-pass polishing mechanism to extend the capacity of NMT via reinforcement learning. More specifically, we adopt an extra policy network to automatically choose a suitable and effective number of decoding passes, according to the complexity of source sentences and the quality of the generated translations. Extensive experiments on Chinese-English translation demonstrate the effectiveness of our proposed adaptive multi-pass decoder upon the conventional NMT with a significant improvement about 1.55 BLEU.
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension
Authors: Sathish Reddy Indurthi, Seunghak Yu, Seohyun Back, Heriberto Cuay{\'a}huitl
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Reading Comprehension

In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer. To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset {`}NarrativeQA{'}. The proposed architecture outperforms state-of-the-art results by 12.62{\%} (ROUGE-L) relative improvement.
Quantifying Context Overlap for Training Word Embeddings
Authors: Yimeng Zhuang, Jinghui Xie, Yinhe Zheng, Xuan Zhu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dimensionality Reduction, Language Modeling, Language Modelling, Learning Word Embeddings, Representation Learning, Semantic Composition, Word Embeddings, Word Similarity

Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations. In this paper, a metric is designed to estimate second order co-occurrence relations based on context overlap. The estimated values are further used as the augmented data to enhance the learning of word embeddings by joint training with existing neural word embedding models. Experimental results show that better word vectors can be obtained for word similarity tasks and some downstream NLP tasks by the enhanced approach.
Unsupervised Bilingual Lexicon Induction via Latent Variable Models
Authors: Zi-Yi Dou, Zhi-Hao Zhou, Shu-Jian Huang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Bilingual Lexicon Induction, Word Embeddings

Bilingual lexicon extraction has been studied for decades and most previous methods have relied on parallel corpora or bilingual dictionaries. Recent studies have shown that it is possible to build a bilingual dictionary by aligning monolingual word embedding spaces in an unsupervised way. With the recent advances in generative models, we propose a novel approach which builds cross-lingual dictionaries via latent variable models and adversarial training with no parallel corpora. To demonstrate the effectiveness of our approach, we evaluate our approach on several language pairs and the experimental results show that our model could achieve competitive and even superior performance compared with several state-of-the-art models.
Learning Unsupervised Word Translations Without Adversaries
Authors: Tanmoy Mukherjee, Makoto Yamada, Timothy Hospedales
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Multilingual Word Embeddings, Sensitivity, Transfer Learning, Translation, Word Embeddings, Word Translation

Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods unsupervised bilingual dictionary induction are based on generative adversarial models, and as such suffer from their well known problems of instability and hyper-parameter sensitivity. We present a statistical dependency-based approach to bilingual dictionary induction that is unsupervised {--} no seed dictionary or parallel corpora required; and introduces no adversary {--} therefore being much easier to train. Our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods.
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification
Authors: Ryo Masumura, Yusuke Shinohara, Ryuichiro Higashinaka, Yushi Aono
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, intent-classification, Intent Classification, Spoken Dialogue Systems

This paper proposes an adversarial training method for the multi-task and multi-lingual joint modeling needed for utterance intent classification. In joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. This is achieved by introducing both language-specific networks shared among different tasks and task-specific networks shared among different languages. However, the shared networks are often specialized in majority tasks or languages, so performance degradation must be expected for some minor data sets. In order to improve the invariance of shared networks, the proposed method introduces both language-specific task adversarial networks and task-specific language adversarial networks; both are leveraged for purging the task or language dependencies of the shared networks. The effectiveness of the adversarial training proposal is demonstrated using Japanese and English data sets for three different utterance intent classification tasks.
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning
Authors: Shiv Shankar, Siddhant Garg, Sunita Sarawagi
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Hard Attention, Image Captioning, Morphological Inflection, Translation

In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.
Joint Learning for Emotion Classification and Emotion Cause Detection
Authors: Ying Chen, Wenjun Hou, Xiyao Cheng, Shoushan Li
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, Emotion Classification, Emotion Recognition, General Classification

We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.
Exploring Optimism and Pessimism in Twitter Using Deep Learning
Authors: Cornelia Caragea, Liviu P. Dinu, Bogdan Dumitru
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Deep Learning

Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning
Authors: Sotiris Lamprinidis, Daniel Hardt, Dirk Hovy
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Multi-Task Learning, Part-Of-Speech Tagging, regression, Word Embeddings

Newspapers need to attract readers with headlines, anticipating their readers{'} preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns.
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates
Authors: Di Chen, Jiachen Du, Lidong Bing, Ruifeng Xu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Natural Language Inference, Sentiment Analysis

Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining. The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates. Previous works usually lack the capability of jointly modeling these two factors. To alleviate this problem, this paper proposes a hybrid neural attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. Experimental results on three (dis)agreement inference datasets show that our model outperforms the state-of-the-art models.
Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information
Authors: Dirk Hovy, Tommaso Fornaciari
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Attribute, Classification, General Classification, Semantic Textual Similarity, text-classification, Text Classification

Most text-classification approaches represent the input based on textual features, either feature-based or continuous. However, this ignores strong non-linguistic similarities like homophily: people within a demographic group use language more similar to each other than to non-group members. We use homophily cues to retrofit text-based author representations with non-linguistic information, and introduce a trade-off parameter. This approach increases in-class similarity between authors, and improves classification performance by making classes more linearly separable. We evaluate the effect of our method on two author-attribute prediction tasks with various training-set sizes and parameter settings. We find that our method can significantly improve classification performance, especially when the number of labels is large and limited labeled data is available. It is potentially applicable as preprocessing step to any text-classification task.
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
Authors: Chen Shi, Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, Lintao Zhang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Active Learning, Clustering

The lack of labeled data is one of the main challenges when building a task-oriented dialogue system. Existing dialogue datasets usually rely on human labeling, which is expensive, limited in size, and in low coverage. In this paper, we instead propose our framework auto-dialabel to automatically cluster the dialogue intents and slots. In this framework, we collect a set of context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. Experimental results show that our framework can promote human labeling cost to a great extent, achieve good intent clustering accuracy (84.1{\%}), and provide reasonable and instructive slot labeling results.
Modeling Temporality of Human Intentions by Domain Adaptation
Authors: Xiaolei Huang, Lixing Liu, Kate Carey, Joshua Woolley, Stefan Scherer, Brian Borsari
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decision Making, Domain Adaptation, General Classification, Intent Classification, Topic Models

Categorizing patient{'}s intentions in conversational assessment can help decision making in clinical treatments. Many conversation corpora span broaden a series of time stages. However, it is not clear that how the themes shift in the conversation impact on the performance of human intention categorization (eg., patients might show different behaviors during the beginning versus the end). This paper proposes a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). We deploy Bi-LSTM and topic model jointly to learn language usage change across different time sessions. We conduct experiments on the MI corpora to show the promising improvement after considering temporality in the classification task.
Out-of-domain Detection based on Generative Adversarial Network
Authors: Seonghan Ryu, Sangjun Koo, Hwanjo Yu, Gary Geunbae Lee
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Generative Adversarial Network, Out of Distribution (OOD) Detection, Sentence Embedding

The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems. We propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. To improve basic GANs, we apply feature matching loss in the discriminator, use domain-category analysis as an additional task in the discriminator, and remove the biases in the generator. Thereby, we reduce the huge effort of collecting OOD sentences for training OOD detection. For evaluation, we experimented OOD detection on a multi-domain dialog system. The experimental results showed the proposed method was most accurate compared to the existing methods.
Listening Comprehension over Argumentative Content
Authors: Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Automatic Speech Recognition (ASR), Machine Reading Comprehension, Question Answering, Reading Comprehension, Speech Recognition

This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.
Using active learning to expand training data for implicit discourse relation recognition
Authors: Yang Xu, Yu Hong, Huibin Ruan, Jianmin Yao, Min Zhang, Guodong Zhou
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Active Learning, Relation, Relation Classification

We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans. Implicit relation recognition is challenging due to the lack of explicit relational clues. The increasingly popular neural network techniques have been proven effective for semantic encoding, whereby widely employed to boost semantic relation discrimination. However, learning to predict semantic relations at a deep level heavily relies on a great deal of training data, but the scale of the publicly available data in this field is limited. In this paper, we follow Rutherford and Xue (2015) to expand the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. On the basis, we carry out an experiment of sampling, in which a simple active learning approach is used, so as to take the informative instances for data expansion. The goal is to verify whether the selective use of external data not only reduces the time consumption of retraining but also ensures a better system performance. Using the expanded training data, we retrain a convolutional neural network (CNN) based classifer which is a simplified version of Qin et al. (2016){'}s stacking gated relation recognizer. Experimental results show that expanding the training set with small-scale carefully-selected external data yields substantial performance gain, with the improvements of about 4{\%} for accuracy and 3.6{\%} for F-score. This allows a weak classifier to achieve a comparable performance against the state-of-the-art systems.