Lei Chen


2020

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PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL Generation
Huajie Wang | Mei Li | Lei Chen
Proceedings of the 28th International Conference on Computational Linguistics

Text-to-SQL is a task of translating utterances to SQL queries, and most existing neural approaches of text-to-SQL focus on the cross-domain context-independent generation task. We pay close attention to the cross-domain context-dependent text-to-SQL generation task, which requires a model to depend on the interaction history and current utterance to generate SQL query. In this paper, we present an encoder-decoder model called PG-GSQL based on the interaction-level encoder and with two effective innovations in decoder to solve cross-domain context-dependent text-to-SQL task. 1) To effectively capture historical information of SQL query and reuse the previous SQL query tokens, we use a hybrid pointer-generator network as decoder to copy tokens from the previous SQL query via pointer, the generator part is utilized to generate new tokens. 2) We propose a guide component to limit the prediction space of vocabulary for avoiding table-column dependency and foreign key dependency errors during decoding phase. In addition, we design a column-table linking mechanism to improve the prediction accuracy of tables. On the challenging cross-domain context-dependent text-to-SQL benchmark SParC, PG-GSQL achieves 34.0% question matching accuracy and 19.0% interaction matching accuracy on the dev set. With BERT augmentation, PG-GSQL obtains 53.1% question matching accuracy and 34.7% interaction matching accuracy on the dev set, outperforms the previous state-of-the-art model by 5.9% question matching accuracy and 5.2% interaction matching accuracy. Our code is publicly available.

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Modeling Evolution of Message Interaction for Rumor Resolution
Lei Chen | Zhongyu Wei | Jing Li | Baohua Zhou | Qi Zhang | Xuanjing Huang
Proceedings of the 28th International Conference on Computational Linguistics

Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately. However, how local interactive pattern affects global information assemblage has not been explored. In this paper, we attempt to address the problem by learning evolution of message interaction. We model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. Moreover, we capture the variation of message interaction using a hierarchical framework to better integrate information flow of a rumor cascade. Experiments on PHEME dataset demonstrate our proposed model achieves higher accuracy than existing methods.

2019

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A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis
Qingnan Jiang | Lei Chen | Ruifeng Xu | Xiang Ao | Min Yang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications. In existing ABSA datasets, most sentences contain only one aspect or multiple aspects with the same sentiment polarity, which makes ABSA task degenerate to sentence-level sentiment analysis. In this paper, we present a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset, in which each sentence contains at least two different aspects with different sentiment polarities. The release of this dataset would push forward the research in this field. In addition, we propose simple yet effective CapsNet and CapsNet-BERT models which combine the strengths of recent NLP advances. Experiments on our new dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods

2018

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CONDUCT: An Expressive Conducting Gesture Dataset for Sound Control
Lei Chen | Sylvie Gibet | Camille Marteau
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Predicting Audience’s Laughter During Presentations Using Convolutional Neural Network
Lei Chen | Chong Min Lee
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Public speakings play important roles in schools and work places and properly using humor contributes to effective presentations. For the purpose of automatically evaluating speakers’ humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The advantages of the CNN method are both getting higher detection accuracies and being able to learn essential features automatically.

2016

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Analyzing Time Series Changes of Correlation between Market Share and Concerns on Companies measured through Search Engine Suggests
Takakazu Imada | Yusuke Inoue | Lei Chen | Syunya Doi | Tian Nie | Chen Zhao | Takehito Utsuro | Yasuhide Kawada
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper proposes how to utilize a search engine in order to predict market shares. We propose to compare rates of concerns of those who search for Web pages among several companies which supply products, given a specific products domain. We measure concerns of those who search for Web pages through search engine suggests. Then, we analyze whether rates of concerns of those who search for Web pages have certain correlation with actual market share. We show that those statistics have certain correlations. We finally propose how to predict the market share of a specific product genre based on the rates of concerns of those who search for Web pages.

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Can We Make Computers Laugh at Talks?
Chong Min Lee | Su-Youn Yoon | Lei Chen
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

Considering the importance of public speech skills, a system which makes a prediction on where audiences laugh in a talk can be helpful to a person who prepares for a talk. We investigated a possibility that a state-of-the-art humor recognition system can be used in detecting sentences inducing laughters in talks. In this study, we used TED talks and laughters in the talks as data. Our results showed that the state-of-the-art system needs to be improved in order to be used in a practical application. In addition, our analysis showed that classifying humorous sentences in talks is very challenging due to close distance between humorous and non-humorous sentences.

2015

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Feature selection for automated speech scoring
Anastassia Loukina | Klaus Zechner | Lei Chen | Michael Heilman
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

2014

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Automatic evaluation of spoken summaries: the case of language assessment
Anastassia Loukina | Klaus Zechner | Lei Chen
Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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Automated scoring of speaking items in an assessment for teachers of English as a Foreign Language
Klaus Zechner | Keelan Evanini | Su-Youn Yoon | Lawrence Davis | Xinhao Wang | Lei Chen | Chong Min Lee | Chee Wee Leong
Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

2013

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Applying Unsupervised Learning To Support Vector Space Model Based Speaking Assessment
Lei Chen
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment
Shasha Xie | Lei Chen
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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Building Comparable Corpora Based on Bilingual LDA Model
Zede Zhu | Miao Li | Lei Chen | Zhenxin Yang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Utilizing Cumulative Logit Model and Human Computation on Automated Speech Assessment
Lei Chen
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Scoring Spoken Responses Based on Content Accuracy
Fei Huang | Lei Chen | Jana Sukkarieh
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

2011

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Detecting Structural Events for Assessing Non-Native Speech
Lei Chen | Su-Youn Yoon
Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications

2010

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Towards Using Structural Events To Assess Non-native Speech
Lei Chen | Joel Tetreault | Xiaoming Xi
Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications

2009

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Automatic Scoring of Children’s Read-Aloud Text Passages and Word Lists
Klaus Zechner | John Sabatini | Lei Chen
Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications

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Improved pronunciation features for construct-driven assessment of non-native spontaneous speech
Lei Chen | Klaus Zechner | Xiaoming Xi
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2006

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An Open Source Prosodic Feature Extraction Tool
Zhongqiang Huang | Lei Chen | Mary Harper
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

There has been an increasing interest in utilizing a wide variety of knowledge sources in order to perform automatic tagging of speech events, such as sentence boundaries and dialogue acts. In addition to the word spoken, the prosodic content of the speech has been proved quite valuable in a variety of spoken language processing tasks such as sentence segmentation and tagging, disfluency detection, dialog act segmentation and tagging, and speaker recognition. In this paper, we report on an open source prosodic feature extraction tool based on Praat, with a description of the prosodic features and the implementation details, as well as a discussion of its extension capability. We also evaluate our tool on a sentence boundary detection task and report the system performance on the NIST RT04 CTS data.

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Incorporating Gesture and Gaze into Multimodal Models of Human-to-Human Communication
Lei Chen
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Doctoral Consortium

2004

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Evaluating Factors Impacting the Accuracy of Forced Alignments in a Multimodal Corpus
Lei Chen | Yang Liu | Mary Harper | Eduardo Maia | Susan McRoy
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

People, when processing human-to-human communication, utilize everything they can in order to understand that communication, including speech and information such as the time and location of an interlocutor's gesture and gaze. Speech and gesture are known to exhibit a synchronous relationship in human communication; however, the precise nature of that relationship requires further investigation. The construction of computer models of multimodal human communication would be enabled by the availability of multimodal communication corpora annotated with synchronized gesture and speech features. To investigate the temporal relationships of these knowledge sources, we have collected and are annotating several multimodal corpora with time-aligned features. Forced alignment between a speech file and its transcription is a crucial part of multimodal corpus production. This paper investigates a number of factors that may contribute to highly accurate forced alignments to support the rapid production of these multimodal corpora including the acoustic model, the match between the speech used for training the system and that to be force aligned, the amount of data used to train the ASR system, the availability of speaker adaptation, and the duration of alignment segments.