Jun Wang


2021

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基于预训练语言模型的繁体古文自动句读研究(Automatic Traditional Ancient Chinese Texts Segmentation and Punctuation Based on Pre-training Language Model)
Xuemei Tang (唐雪梅) | Qi Su (苏祺) | Jun Wang (王军) | Yuhang Chen (陈雨航) | Hao Yang (杨浩)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

“未经整理的古代典籍不含任何标点,不符合当代人的阅读习惯,古籍断句标点之后有助于阅读、研究和出版。本文提出了一种基于预训练语言模型的繁体古文自动句读框架。本文整理了约10亿字的繁体古文语料,对于训练语言模型进行增量训练,在此基础上上实现古文自动句读和标点。实验表明经过大规模繁体古文语料增量训练后的语言模型具备更好的古文语义表示能力,能够有助提升繁体古文自动句读和自动标点的效果。融合了增量训练模型之后,古文断句F1值达到95.03%,古文标点F1值达到了80.18%,分别比使用未增量训练的语言模型提升1.83%和2.21%。为解决现有篇章级句读方案效率低的问题,本文改进了前人的串行滑动窗口方式,在一定程度上提高了句读效率,并提出一种新的并行滑动窗口方式,能够高效准确地进行长文本自动句读。”

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Self Promotion in US Congressional Tweets
Jun Wang | Kelly Cui | Bei Yu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Prior studies have found that women self-promote less than men due to gender stereotypes. In this study we built a BERT-based NLP model to predict whether a Congressional tweet shows self-promotion or not and then used this model to examine whether a gender gap in self-promotion exists among Congressional tweets. After analyzing 2 million Congressional tweets from July 2017 to March 2021, controlling for a number of factors that include political party, chamber, age, number of terms in Congress, number of daily tweets, and number of followers, we found that women in Congress actually perform more self-promotion on Twitter, indicating a reversal of traditional gender norms where women self-promote less than men.

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MM-AVS: A Full-Scale Dataset for Multi-modal Summarization
Xiyan Fu | Jun Wang | Zhenglu Yang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks. However, its learning materials have been lacking a holistic organization by integrating resources from various modalities, thereby lagging behind the research progress of this field. In this study, we release a full-scale multimodal dataset comprehensively gathering documents, summaries, images, captions, videos, audios, transcripts, and titles in English from CNN and Daily Mail. To our best knowledge, this is the first collection that spans all modalities and nearly comprises all types of materials available in this community. In addition, we devise a baseline model based on the novel dataset, which employs a newly proposed Jump-Attention mechanism based on transcripts. The experimental results validate the important assistance role of the external information for multimodal summarization.

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Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning
Jun Wang | Chang Xu | Francisco Guzmán | Ahmed El-Kishky | Yuqing Tang | Benjamin Rubinstein | Trevor Cohn
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation
Jun Wang | Chang Xu | Francisco Guzmán | Ahmed El-Kishky | Benjamin Rubinstein | Trevor Cohn
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Measuring Correlation-to-Causation Exaggeration in Press Releases
Bei Yu | Jun Wang | Lu Guo | Yingya Li
Proceedings of the 28th International Conference on Computational Linguistics

Press releases have an increasingly strong influence on media coverage of health research; however, they have been found to contain seriously exaggerated claims that can misinform the public and undermine public trust in science. In this study we propose an NLP approach to identify exaggerated causal claims made in health press releases that report on observational studies, which are designed to establish correlational findings, but are often exaggerated as causal. We developed a new corpus and trained models that can identify causal claims in the main statements in a press release. By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22% of press releases made exaggerated causal claims from correlational findings in observational studies. Furthermore, universities exaggerated more often than journal publishers by a ratio of 1.5 to 1. Encouragingly, the exaggeration rate has slightly decreased over the past 10 years, despite the increase of the total number of press releases. More research is needed to understand the cause of the decreasing pattern.

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Diversify Question Generation with Continuous Content Selectors and Question Type Modeling
Zhen Wang | Siwei Rao | Jie Zhang | Zhen Qin | Guangjian Tian | Jun Wang
Findings of the Association for Computational Linguistics: EMNLP 2020

Generating questions based on answers and relevant contexts is a challenging task. Recent work mainly pays attention to the quality of a single generated question. However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression. In this paper, we explore the diversity of question generation and come up with methods from these two aspects. Specifically, we relate contextual focuses with content selectors, which are modeled by a continuous latent variable with the technique of conditional variational auto-encoder (CVAE). In the realization of CVAE, a multimodal prior distribution is adopted to allow for more diverse content selectors. To take into account various means of expression, question types are explicitly modeled and a diversity-promoting algorithm is proposed further. Experimental results on public datasets show that our proposed method can significantly improve the diversity of generated questions, especially from the perspective of using different question types. Overall, our proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches.

2019

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Detecting Causal Language Use in Science Findings
Bei Yu | Yingya Li | Jun Wang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Causal interpretation of correlational findings from observational studies has been a major type of misinformation in science communication. Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. In this study, we first annotated a corpus of over 3,000 PubMed research conclusion sentences, then developed a BERT-based prediction model that classifies conclusion sentences into “no relationship”, “correlational”, “conditional causal”, and “direct causal” categories, achieving an accuracy of 0.90 and a macro-F1 of 0.88. We then applied the prediction model to measure the causal language use in the research conclusions of about 38,000 observational studies in PubMed. The prediction result shows that 21.7% studies used direct causal language exclusively in their conclusions, and 32.4% used some direct causal language. We also found that the ratio of causal language use differs among authors from different countries, challenging the notion of a shared consensus on causal language use in the global science community. Our prediction model could also be used to help identify the inappropriate use of causal language in science publications.

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Permanent Magnetic Articulograph (PMA) vs Electromagnetic Articulograph (EMA) in Articulation-to-Speech Synthesis for Silent Speech Interface
Beiming Cao | Nordine Sebkhi | Ted Mau | Omer T. Inan | Jun Wang
Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies

Silent speech interfaces (SSIs) are devices that enable speech communication when audible speech is unavailable. Articulation-to-speech (ATS) synthesis is a software design in SSI that directly converts articulatory movement information into audible speech signals. Permanent magnetic articulograph (PMA) is a wireless articulator motion tracking technology that is similar to commercial, wired Electromagnetic Articulograph (EMA). PMA has shown great potential for practical SSI applications, because it is wireless. The ATS performance of PMA, however, is unknown when compared with current EMA. In this study, we compared the performance of ATS using a PMA we recently developed and a commercially available EMA (NDI Wave system). Datasets with same stimuli and size that were collected from tongue tip were used in the comparison. The experimental results indicated the performance of PMA was close to, although not as equally good as that of EMA. Furthermore, in PMA, converting the raw magnetic signals to positional signals did not significantly affect the performance of ATS, which support the future direction in PMA-based ATS can be focused on the use of positional signals to maximize the benefit of spatial analysis.

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Speech-based Estimation of Bulbar Regression in Amyotrophic Lateral Sclerosis
Alan Wisler | Kristin Teplansky | Jordan Green | Yana Yunusova | Thomas Campbell | Daragh Heitzman | Jun Wang
Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurological disease that leads to degeneration of motor neurons and, as a result, inhibits the ability of the brain to control muscle movements. Monitoring the progression of ALS is of fundamental importance due to the wide variability in disease outlook that exists across patients. This progression is typically tracked using the ALS functional rating scale - revised (ALSFRS-R), which is the current clinical assessment of a patient’s level of functional impairment including speech and other motor tasks. In this paper, we investigated automatic estimation of the ALSFRS-R bulbar subscore from acoustic and articulatory movement samples. Experimental results demonstrated the AFSFRS-R bulbar subscore can be predicted from speech samples, which has clinical implication for automatic monitoring of the disease progression of ALS using speech information.

2018

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JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features
Hongru Liang | Haozheng Wang | Jun Wang | Shaodi You | Zhe Sun | Jin-Mao Wei | Zhenglu Yang
Proceedings of the 27th International Conference on Computational Linguistics

Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrate our proposed model outperforms the state-of-the-art approaches by a large margin.

2015

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Recognizing Dysarthric Speech due to Amyotrophic Lateral Sclerosis with Across-Speaker Articulatory Normalization
Seongjun Hahm | Daragh Heitzman | Jun Wang
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition
Jun Wang | Seongjun Hahm | Ted Mau
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

2014

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Preliminary Test of a Real-Time, Interactive Silent Speech Interface Based on Electromagnetic Articulograph
Jun Wang | Ashok Samal | Jordan Green
Proceedings of the 5th Workshop on Speech and Language Processing for Assistive Technologies

2013

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Word Recognition from Continuous Articulatory Movement Time-series Data using Symbolic Representations
Jun Wang | Arvind Balasubramanian | Luis Mojica de la Vega | Jordan R. Green | Ashok Samal | Balakrishnan Prabhakaran
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies

2008

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A Data Driven Approach to Query Expansion in Question Answering
Leon Derczynski | Jun Wang | Robert Gaizauskas | Mark A. Greenwood
Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering

2003

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On Intra-page and Inter-page Semantic Analysis of Web Pages
Jun Wang | Jicheng Wang | Gangshan Wu | Hiroshi Tsuda
Proceedings of the 17th Pacific Asia Conference on Language, Information and Computation