Jun Wang


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IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks
Xuanli He | Jun Wang | Benjamin Rubinstein | Trevor Cohn
Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)

Backdoor attacks are an insidious security threat against machine learning models. Adversaries can manipulate the predictions of compromised models by inserting triggers into the training phase. Various backdoor attacks have been devised which can achieve nearly perfect attack success without affecting model predictions for clean inputs. Means of mitigating such vulnerabilities are underdeveloped, especially in natural language processing. To fill this gap, we introduce IMBERT, which uses either gradients or self-attention scores derived from victim models to self-defend against backdoor attacks at inference time. Our empirical studies demonstrate that IMBERT can effectively identify up to 98.5% of inserted triggers. Thus, it significantly reduces the attack success rate while attaining competitive accuracy on the clean dataset across widespread insertion-based attacks compared to two baselines. Finally, we show that our approach is model-agnostic, and can be easily ported to several pre-trained transformer models.

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Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
Yiqun Hu | Yiyun Zhao | Jiarong Jiang | Wuwei Lan | Henghui Zhu | Anuj Chauhan | Alexander Hanbo Li | Lin Pan | Jun Wang | Chung-Wei Hang | Sheng Zhang | Jiang Guo | Mingwen Dong | Joseph Lilien | Patrick Ng | Zhiguo Wang | Vittorio Castelli | Bing Xiang
Findings of the Association for Computational Linguistics: ACL 2023

There has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data. We observed three shortcomings: illogical synthetic SQL queries from independent column sampling, arbitrary table joins, and language gaps between the synthesized SQL and natural language question (NLQ) pair. To address these issues, we propose a novel synthesis framework that imposes strong typing constraints, incorporates key relationships from schema, and conducts schema-distance-weighted column sampling. We also adopt an intermediate representation (IR) for the SQL-to-text task to further improve the quality of the generated NLQ. When existing powerful text-to-SQL parsers are pretrained on our high-quality synthesized data, these models have significant accuracy boosts and achieve new state-of-the-art performance on Spider. We also demonstrate the effectiveness of our techniques with ablation studies

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ACROSS: An Alignment-based Framework for Low-Resource Many-to-One Cross-Lingual Summarization
Peiyao Li | Zhengkun Zhang | Jun Wang | Liang Li | Adam Jatowt | Zhenglu Yang
Findings of the Association for Computational Linguistics: ACL 2023

This research addresses the challenges of Cross-Lingual Summarization (CLS) in low-resource scenarios and over imbalanced multilingual data. Existing CLS studies mostly resort to pipeline frameworks or multi-task methods in bilingual settings. However, they ignore the data imbalance in multilingual scenarios and do not utilize the high-resource monolingual summarization data. In this paper, we propose the Aligned CROSs-lingual Summarization (ACROSS) model to tackle these issues. Our framework aligns low-resource cross-lingual data with high-resource monolingual data via contrastive and consistency loss, which help enrich low-resource information for high-quality summaries. In addition, we introduce a data augmentation method that can select informative monolingual sentences, which facilitates a deep exploration of high-resource information and introduce new information for low-resource languages. Experiments on the CrossSum dataset show that ACROSS outperforms baseline models and obtains consistently dominant performance on 45 language pairs.

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CCL23-Eval任务1总结报告:古籍命名实体识别(GuNER2023)(Overview of CCL23-Eval Task 1: Named Entity Recognition in Ancient Chinese Books)
Qi Su (祺苏,) | Yingying Wang (王莹莹) | Zekun Deng (邓泽琨) | Hao Yang (杨浩) | Jun Wang (王军)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)


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MGR: Multi-generator Based Rationalization
Wei Liu | Haozhao Wang | Jun Wang | Ruixuan Li | Xinyang Li | YuanKai Zhang | Yang Qiu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input text to the following predictor. However, rationalization suffers from two key challenges, i.e., spurious correlation and degeneration, where the predictor overfits the spurious or meaningless pieces solely selected by the not-yet well-trained generator and in turn deteriorates the generator. Although many studies have been proposed to address the two challenges, they are usually designed separately and do not take both of them into account. In this paper, we propose a simple yet effective method named MGR to simultaneously solve the two problems. The key idea of MGR is to employ multiple generators such that the occurrence stability of real pieces is improved and more meaningful pieces are delivered to the predictor. Empirically, we show that MGR improves the F1 score by up to 20.9% as compared to state-of-the-art methods.

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XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
Yusen Zhang | Jun Wang | Zhiguo Wang | Rui Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Cross-Lingual Semantic Parsing (CLSP) aims to translate queries in multiple natural languages (NLs) into meaning representations (MRs) such as SQL, lambda calculus, and logic forms. However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensive and unified evaluation of CLSP on a diverse range of NLs and MRs. To this end, we present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations by examining and selecting 9 existing datasets to cover 5 tasks and 164 domains. We use XSemPLR to conduct a comprehensive benchmark study on a wide range of multilingual language models including encoder-based models (mBERT, XLM-R), encoder-decoder models (mBART, mT5), and decoder-based models (Codex, BLOOM). We design 6 experiment settings covering various lingual combinations (monolingual, multilingual, cross-lingual) and numbers of learning samples (full dataset, few-shot, and zero-shot). Our experiments show that encoder-decoder models (mT5) achieve the highest performance compared with other popular models, and multilingual training can further improve the average performance. Notably, multilingual large language models (e.g., BLOOM) are still inadequate to perform CLSP tasks. We also find that the performance gap between monolingual training and cross-lingual transfer learning is still significant for multilingual models, though it can be mitigated by cross-lingual few-shot training. Our dataset and code are available at https://github.com/psunlpgroup/XSemPLR.


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PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning
Qizhi Lin | Changyu Hou | Xiaopeng Wang | Jun Wang | Yixuan Qiao | Peng Jiang | Xiandi Jiang | Benqi Wang | Qifeng Xiao
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

From pretrained contextual embedding to document-level embedding, the selection and construction of embedding have drawn more and more attention in the NER domain in recent research. This paper aims to discuss the performance of ensemble embeddings on complex NER tasks. Enlightened by Wang’s methodology, we try to replicate the dominating power of ensemble models with reinforcement learning optimizor on plain NER tasks to complex ones. Based on the composition of semeval dataset, the performance of the applied model is tested on lower-context, QA, and search query scenarios together with its zero-shot learning ability. Results show that with abundant training data, the model can achieve similar performance on lower-context cases compared to plain NER cases, but can barely transfer the performance to other scenarios in the test phase.

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SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models
Changyu Hou | Jun Wang | Yixuan Qiao | Peng Jiang | Peng Gao | Guotong Xie | Qizhi Lin | Xiaopeng Wang | Xiandi Jiang | Benqi Wang | Qifeng Xiao
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Large scale pre-training models have been widely used in named entity recognition (NER) tasks. However, model ensemble through parameter averaging or voting can not give full play to the differentiation advantages of different models, especially in the open domain. This paper describes our NER system in the SemEval 2022 task11: MultiCoNER. We proposed an effective system to adaptively ensemble pre-trained language models by a Transformer layer. By assigning different weights to each model for different inputs, we adopted the Transformer layer to integrate the advantages of diverse models effectively. Experimental results show that our method achieves superior performances in Farsi and Dutch.

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数字人文视角下的《史记》《汉书》比较研究(A Comparative Study of Shiji and Hanshu from the Perspective of Digital Humanities)
Zekun Deng (邓泽琨) | Hao Yang (杨浩) | Jun Wang (王军)
Proceedings of the 21st Chinese National Conference on Computational Linguistics


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Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems
Ling.Yu Zhu | Zhengkun Zhang | Jun Wang | Hongbin Wang | Haiying Wu | Zhenglu Yang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multi-party dialogues, however, are pervasive in reality. Furthermore, emotion and sensibility are typically confused; a refined empathy analysis is needed for comprehending fragile and nuanced human feelings. We address these issues by proposing a novel task called Multi-Party Empathetic Dialogue Generation in this study. Additionally, a Static-Dynamic model for Multi-Party Empathetic Dialogue Generation, SDMPED, is introduced as a baseline by exploring the static sensibility and dynamic emotion for the multi-party empathetic dialogue learning, the aspects that help SDMPED achieve the state-of-the-art performance.

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Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech
Yang Li | Cheng Yu | Guangzhi Sun | Hua Jiang | Fanglei Sun | Weiqin Zu | Ying Wen | Yang Yang | Jun Wang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems. In this paper, a cross-utterance conditional VAE (CUC-VAE) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features obtained from both past and future sentences. At inference time, instead of the standard Gaussian distribution used by VAE, CUC-VAE allows sampling from an utterance-specific prior distribution conditioned on cross-utterance information, which allows the prosody features generated by the TTS system to be related to the context and is more similar to how humans naturally produce prosody. The performance of CUC-VAE is evaluated via a qualitative listening test for naturalness, intelligibility and quantitative measurements, including word error rates and the standard deviation of prosody attributes. Experimental results on LJ-Speech and LibriTTS data show that the proposed CUC-VAE TTS system improves naturalness and prosody diversity with clear margins.

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Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension
Huibin Zhang | Zhengkun Zhang | Yao Zhang | Jun Wang | Yufan Li | Ning Jiang | Xin Wei | Zhenglu Yang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step. Comprehending PMDs and inducing their representations for the downstream reasoning tasks is designated as Procedural MultiModal Machine Comprehension (M3C). In this study, we approach Procedural M3C at a fine-grained level (compared with existing explorations at a document or sentence level), that is, entity. With delicate consideration, we model entity both in its temporal and cross-modal relation and propose a novel Temporal-Modal Entity Graph (TMEG). Specifically, graph structure is formulated to capture textual and visual entities and trace their temporal-modal evolution. In addition, a graph aggregation module is introduced to conduct graph encoding and reasoning. Comprehensive experiments across three Procedural M3C tasks are conducted on a traditional dataset RecipeQA and our new dataset CraftQA, which can better evaluate the generalization of TMEG.

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Measuring and Mitigating Name Biases in Neural Machine Translation
Jun Wang | Benjamin Rubinstein | Trevor Cohn
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Neural Machine Translation (NMT) systems exhibit problematic biases, such as stereotypical gender bias in the translation of occupation terms into languages with grammatical gender. In this paper we describe a new source of bias prevalent in NMT systems, relating to translations of sentences containing person names. To correctly translate such sentences, a NMT system needs to determine the gender of the name. We show that leading systems are particularly poor at this task, especially for female given names. This bias is deeper than given name gender: we show that the translation of terms with ambiguous sentiment can also be affected by person names, and the same holds true for proper nouns denoting race. To mitigate these biases we propose a simple but effective data augmentation method based on randomly switching entities during translation, which effectively eliminates the problem without any effect on translation quality.

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Eureka: Neural Insight Learning for Knowledge Graph Reasoning
Alex X. Zhang | Xun Liang | Bo Wu | Xiangping Zheng | Sensen Zhang | Yuhui Guo | Jun Wang | Xinyao Liu
Proceedings of the 29th International Conference on Computational Linguistics

The human recognition system has presented the remarkable ability to effortlessly learn novel knowledge from only a few trigger events based on prior knowledge, which is called insight learning. Mimicking such behavior on Knowledge Graph Reasoning (KGR) is an interesting and challenging research problem with many practical applications. Simultaneously, existing works, such as knowledge embedding and few-shot learning models, have been limited to conducting KGR in either “seen-to-seen” or “unseen-to-unseen” scenarios. To this end, we propose a neural insight learning framework named Eureka to bridge the “seen” to “unseen” gap. Eureka is empowered to learn the seen relations with sufficient training triples while providing the flexibility of learning unseen relations given only one trigger without sacrificing its performance on seen relations. Eureka meets our expectation of the model to acquire seen and unseen relations at no extra cost, and eliminate the need to retrain when encountering emerging unseen relations. Experimental results on two real-world datasets demonstrate that the proposed framework also outperforms various state-of-the-art baselines on datasets of both seen and unseen relations.

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Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding
Jun Wang | Patrick Ng | Alexander Hanbo Li | Jiarong Jiang | Zhiguo Wang | Bing Xiang | Ramesh Nallapati | Sudipta Sengupta
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance. In addition, without lexical-level fine-grained query understanding, linking between query and database can only rely on fuzzy string match which leads to suboptimal performance in real applications. In view of this, in this paper we present a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural semantic parser (NSP). By jointly modeling query and database, NER model analyzes user intents and identifies entities in the query. NEL model links typed entities to schema and cell values in database. Parser model leverages available semantic information and linking results and synthesizes tree-structured SQL queries based on dynamically generated grammar. Experiments on SQUALL, a newly released semantic parsing dataset, show that we can achieve 56.8% execution accuracy on WikiTableQuestions (WTQ) test set, which outperforms the state-of-the-art model by 2.7%.

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Foiling Training-Time Attacks on Neural Machine Translation Systems
Jun Wang | Xuanli He | Benjamin Rubinstein | Trevor Cohn
Findings of the Association for Computational Linguistics: EMNLP 2022

Neural machine translation (NMT) systems are vulnerable to backdoor attacks, whereby an attacker injects poisoned samples into training such that a trained model produces malicious translations. Nevertheless, there is little research on defending against such backdoor attacks in NMT. In this paper, we first show that backdoor attacks that have been successful in text classification are also effective against machine translation tasks. We then present a novel defence method that exploits a key property of most backdoor attacks: namely the asymmetry between the source and target language sentences, which is used to facilitate malicious text insertions, substitutions and suchlike. Our technique uses word alignment coupled with language model scoring to detect outlier tokens, and thus can find and filter out training instances which may contain backdoors. Experimental results demonstrate that our technique can significantly reduce the success of various attacks by up to 89.0%, while not affecting predictive accuracy.


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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

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Mitigating Data Poisoning in Text Classification with Differential Privacy
Chang Xu | Jun Wang | Francisco Guzmán | Benjamin Rubinstein | Trevor Cohn
Findings of the Association for Computational Linguistics: EMNLP 2021

NLP models are vulnerable to data poisoning attacks. One type of attack can plant a backdoor in a model by injecting poisoned examples in training, causing the victim model to misclassify test instances which include a specific pattern. Although defences exist to counter these attacks, they are specific to an attack type or pattern. In this paper, we propose a generic defence mechanism by making the training process robust to poisoning attacks through gradient shaping methods, based on differentially private training. We show that our method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive accuracy.

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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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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


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Detecting Health Advice in Medical Research Literature
Yingya Li | Jun Wang | Bei Yu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. However, no current health information system supports the direct retrieval of health advice. This study fills the gap by developing and validating an NLP-based prediction model for identifying health advice in research publications. We annotated a corpus of 6,000 sentences extracted from structured abstracts in PubMed publications as ‘“strong advice”, “weak advice”, or “no advice”, and developed a BERT-based model that can predict, with a macro-averaged F1-score of 0.93, whether a sentence gives strong advice, weak advice, or not. The prediction model generalized well to sentences in both unstructured abstracts and discussion sections, where health advice normally appears. We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.


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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.

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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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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.

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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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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.


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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


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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


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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


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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


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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