Lei Chen


Automatically Detecting Reduced-formed English Pronunciations by Using Deep Learning
Lei Chen | Chenglin Jiang | Yiwei Gu | Yang Liu | Jiahong Yuan
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

Reduced form pronunciations are widely used by native English speakers, especially in casual conversations. Second language (L2) learners have difficulty in processing reduced form pronunciations in listening comprehension and face challenges in production too. Meanwhile, training applications dedicated to reduced forms are still few. To solve this issue, we report on our first effort of using deep learning to evaluate L2 learners’ reduced form pronunciations. Compared with a baseline solution that uses an ASR to determine regular or reduced-formed pronunciations, a classifier that learns representative features via a convolution neural network (CNN) on low-level acoustic features, yields higher detection performance. F-1 metric has been increased from $0.690$ to $0.757$ on the reduction task. Furthermore, adding word entities to compute attention weights to better adjust the features learned by the CNN model helps increasing F-1 to $0.763$.

Utilizing Cross-Modal Contrastive Learning to Improve Item Categorization BERT Model
Lei Chen | Hou Wei Chou
Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

Item categorization (IC) is a core natural language processing (NLP) task in e-commerce. As a special text classification task, fine-tuning pre-trained models, e.g., BERT, has become a mainstream solution. To improve IC performance further, other product metadata, e.g., product images, have been used. Although multimodal IC (MIC) systems show higher performance, expanding from processing text to more resource-demanding images brings large engineering impacts and hinders the deployment of such dual-input MIC systems. In this paper, we proposed a new way of using product images to improve text-only IC model: leveraging cross-modal signals between products’ titles and associated images to adapt BERT models in a self-supervised learning (SSL) way. Our experiments on the three genres in the public Amazon product dataset show that the proposed method generates improved prediction accuracy and macro-F1 values than simply using the original BERT. Moreover, the proposed method is able to keep using existing text-only IC inference implementation and shows a resource advantage than the deployment of a dual-input MIC system.

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
Jiawei Zhou | Xiaoguang Li | Lifeng Shang | Lan Luo | Ke Zhan | Enrui Hu | Xinyu Zhang | Hao Jiang | Zhao Cao | Fan Yu | Xin Jiang | Qun Liu | Lei Chen
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR). However, there still remains a large discrepancy between the provided upstream signals and the downstream question-passage relevance, which leads to less improvement. To bridge this gap, we propose the HyperLink-induced Pre-training (HLP), a method to pre-train the dense retriever with the text relevance induced by hyperlink-based topology within Web documents. We demonstrate that the hyperlink-based structures of dual-link and co-mention can provide effective relevance signals for large-scale pre-training that better facilitate downstream passage retrieval. We investigate the effectiveness of our approach across a wide range of open-domain QA datasets under zero-shot, few-shot, multi-hop, and out-of-domain scenarios. The experiments show our HLP outperforms the BM25 by up to 7 points as well as other pre-training methods by more than 10 points in terms of top-20 retrieval accuracy under the zero-shot scenario. Furthermore, HLP significantly outperforms other pre-training methods under the other scenarios.

Developing Prefix-Tuning Models for Hierarchical Text Classification
Lei Chen | Houwei Chou | Xiaodan Zhu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Hierarchical text classification (HTC) is a key problem and task in many industrial applications, which aims to predict labels organized in a hierarchy for given input text. For example, HTC can group the descriptions of online products into a taxonomy or organizing customer reviews into a hierarchy of categories. In real-life applications, while Pre-trained Language Models (PLMs) have dominated many NLP tasks, they face significant challenges too—the conventional fine-tuning process needs to modify and save models with a huge number of parameters. This is becoming more critical for HTC in both global and local modelling—the latter needs to learn multiple classifiers at different levels/nodes in a hierarchy. The concern will be even more serious since PLM sizes are continuing to increase in order to attain more competitive performances. Most recently, prefix tuning has become a very attractive technology by only tuning and saving a tiny set of parameters. Exploring prefix turning for HTC is hence highly desirable and has timely impact. In this paper, we investigate prefix tuning on HTC in two typical setups: local and global HTC. Our experiment shows that the prefix-tuning model only needs less than 1% of parameters and can achieve performance comparable to regular full fine-tuning. We demonstrate that using contrastive learning in learning prefix vectors can further improve HTC performance.

RIT Boston at SemEval-2022 Task 5: Multimedia Misogyny Detection By Using Coherent Visual and Language Features from CLIP Model and Data-centric AI Principle
Lei Chen | Hou Wei Chou
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Detecting MEME images to be misogynous or not is an application useful on curbing online hateful information against women. In the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) challenge, we designed a system using two simple but effective principles. First, we leverage on recently emerging Transformer models pre-trained (mostly in a self-supervised learning way) on massive data sets to obtain very effective visual (V) and language (L) features. In particular, we used the CLIP model provided by OpenAI to obtain coherent V and L features and then simply used a logistic regression model to make binary predictions. Second, we emphasized more on data rather than tweaking models by following the data-centric AI principle. These principles were proven to be useful and our final macro-F1 is 0.778 for the MAMI task A and ranked the third place among participant teams.

A Progressive Framework for Role-Aware Rumor Resolution
Lei Chen | Guanying Li | Zhongyu Wei | Yang Yang | Baohua Zhou | Qi Zhang | Xuanjing Huang
Proceedings of the 29th International Conference on Computational Linguistics

Existing works on rumor resolution have shown great potential in recognizing word appearance and user participation. However, they ignore the intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. To exploit the fine-grained rumor diffusion patterns and generalize rumor resolution methods, we formulate a predecessor task to identify triggering posts, and then exploit their characteristics to facilitate rumor verification. We design a tree-structured annotation interface and extend PHEME dataset with labels on the message level. Data analysis shows that triggers play a critical role in verifying rumors and present similar lingual patterns across irrelevant events. We propose a graph-based model considering the direction and interaction of information flow to implement role-aware rumor resolution. Experimental results demonstrate the effectiveness of our proposed model and progressive scheme.


Multimodal Item Categorization Fully Based on Transformer
Lei Chen | Houwei Chou | Yandi Xia | Hirokazu Miyake
Proceedings of the 4th Workshop on e-Commerce and NLP

The Transformer has proven to be a powerful feature extraction method and has gained widespread adoption in natural language processing (NLP). In this paper we propose a multimodal item categorization (MIC) system solely based on the Transformer for both text and image processing. On a multimodal product data set collected from a Japanese e-commerce giant, we tested a new image classification model based on the Transformer and investigated different ways of fusing bi-modal information. Our experimental results on real industry data showed that the Transformer-based image classifier has performance on par with ResNet-based classifiers and is four times faster to train. Furthermore, a cross-modal attention layer was found to be critical for the MIC system to achieve performance gains over text-only and image-only models.

Align Voting Behavior with Public Statements for Legislator Representation Learning
Xinyi Mou | Zhongyu Wei | Lei Chen | Shangyi Ning | Yancheng He | Changjian Jiang | Xuanjing Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Ideology of legislators is typically estimated by ideal point models from historical records of votes. It represents legislators and legislation as points in a latent space and shows promising results for modeling voting behavior. However, it fails to capture more specific attitudes of legislators toward emerging issues and is unable to model newly-elected legislators without voting histories. In order to mitigate these two problems, we explore to incorporate both voting behavior and public statements on Twitter to jointly model legislators. In addition, we propose a novel task, namely hashtag usage prediction to model the ideology of legislators on Twitter. In practice, we construct a heterogeneous graph for the legislative context and use relational graph neural networks to learn the representation of legislators with the guidance of historical records of their voting and hashtag usage. Experiment results indicate that our model yields significant improvements for the task of roll call vote prediction. Further analysis further demonstrates that legislator representation we learned captures nuances in statements.

Label-Guided Learning for Item Categorization in e-Commerce
Lei Chen | Hirokazu Miyake
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Item categorization is an important application of text classification in e-commerce due to its impact on the online shopping experience of users. One class of text classification techniques that has gained attention recently is using the semantic information of the labels to guide the classification task. We have conducted a systematic investigation of the potential benefits of these methods on a real data set from a major e-commerce company in Japan. Furthermore, using a hyperbolic space to embed product labels that are organized in a hierarchical structure led to better performance compared to using a conventional Euclidean space embedding. These findings demonstrate how label-guided learning can improve item categorization systems in the e-commerce domain.


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.

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.


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


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)


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.


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.

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.


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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


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

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


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

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

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)


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

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


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


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


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

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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


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


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.