Wei-Fan Chen

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Sonderforschungsbereich 901

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Computational Social Science

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VCard

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+49 5251 60-6839

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cwf(at)mail.upb(dot)de

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ZM2.03-11

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2021

Belief-based Generation of Argumentative Claims

M. Alshomary, W. Chen, T. Gurcke, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics, 2021, pp. 8

Abstract

When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we additionally evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances, but demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.

2020

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

W. Chen, K. Al-Khatib, H. Wachsmuth, B. Stein, in: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020, pp. 149-154

Abstractive Snippet Generation

W. Chen, S. Syed, B. Stein, M. Hagen, M. Potthast, in: Proceedings of the the Web Conference 2020, 2020, pp. 1309-1319

Task Proposal: Abstractive Snippet Generation for Web Pages

S. Syed, W. Chen, M. Hagen, B. Stein, H. Wachsmuth, M. Potthast, in: Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020), 2020, pp. 237-241

Detecting Media Bias in News Articles using Gaussian Bias Distributions

W. Chen, K. Al-Khatib, B. Stein, H. Wachsmuth, in: Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 4290-4300

2019

Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition

W. Chen, K. Al-Khatib, M. Hagen, H. Wachsmuth, B. Stein, in: Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom, 2019, pp. 76-82

2018

中文情感語意分析套件 CSentiPackage 發展與應用

W. Chen, L. Ku, 圖書館學與資訊科學 (2018)

Learning to Flip the Bias of News Headlines

W. Chen, H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of the 11th International Conference on Natural Language Generation, Association for Computational Linguistics, 2018, pp. 79-88

A Plan for Ancillary Copyright: Original Snippets.

M. Potthast, W. Chen, M. Hagen, B. Stein, in: Proceedings of the Second International Workshop on Recent Trends in News Information Retrieval, 2018, pp. 3-5

A User Study on Snippet Generation: Text Reuse vs. Paraphrases

W. Chen, M. Hagen, B. Stein, M. Potthast, in: Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018, pp. 1033-1036

We Like, We Post: A Joint User-Post Approach for Facebook Post Stance Labeling

W. Chen, L. Ku, IEEE Transactions on Knowledge and Data Engineering (2018), 30(10), pp. 2013-2023

Application of Sentiment Analysis to Language Learning

M. Chen, W. Chen, L. Ku, IEEE Access (2018), 6, pp. 24433-24442

2017

Unit Segmentation of Argumentative Texts

Y. Ajjour, W. Chen, J. Kiesel, H. Wachsmuth, B. Stein, in: Proceedings of the 4th Workshop on Argument Mining, 2017, pp. 118-128

How to Get Endorsements? Predicting Facebook Likes Using Post Content and User Engagement

W. Chen, Y. Chen, L. Ku, in: International Conference on HCI in Business, Government, and Organizations, 2017, pp. 190-202

2016

Chinese Textual Sentiment Analysis: Datasets, Resources and Tools

L. Ku, W. Chen. Chinese Textual Sentiment Analysis: Datasets, Resources and Tools. 2016.

UTCNN: a Deep Learning Model of Stance Classification on Social Media Text

W. Chen, L. Ku, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics, 2016, pp. 1635-1645

WordForce: Visualizing Controversial Words in Debates

W. Chen, F. Lin, L. Ku, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, 2016, pp. 273-277

2015

Technology Enhanced Emotion Expression Learning

.M. Chen, W. Chen, .L. Ku, in: Proceedings of the sixth joint Foreign Language Education and Technology Conference (FLEAT VI), 2015

Mining Supportive and Unsupportive Evidence from Facebook Using Anti-reconstruction of the Nuclear Power Plant as an Example

W. Chen, L. Ku, Y. Lee, in: 2015 AAAI Spring Symposium Series, 2015

Topic-based Stance Mining for Social Media Texts

W. Chen, Y. Lee, L. Ku, in: International Conference on HCI in Business, 2015, pp. 22-33

A Computer-assistance Learning System for Emotional Wording

W. Chen, M. Chen, M. Chen, L. Ku, IEEE Transactions on Knowledge and Data Engineering (2015), 28(5), pp. 1093-1104

Embarrassed or Awkward? Ranking Emotion Synonyms for ESL Learners’ Appropriate Wording

W. Chen, M. Chen, L. Ku, in: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp. 144-153

2014

RESOLVE: An Emotion Word Suggestion System Facilitates Language Learners’ Emotional Expressions

M. Chen, W. Chen, L. Ku, in: Proceedings of the AsiaCALL 2014, 2014

2012

A Syllable-based Prosody Modeling for L1 and L2 English Speeches

W. Chen, C. Kuo, Y. Wang, S. Chen, in: Proceedings of the 8th International Symposium on Chinese Spoken Language Processing, IEEE, 2012, pp. 281-285

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