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Sunny start to the new semester (April 2023). Show image information

Sunny start to the new semester (April 2023).

Photo: Paderborn University, Besim Mazhiqi

Wei-Fan Chen

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 Wei-Fan Chen

Sonderforschungsbereich 901

Member - Research Associate

Computational Social Science

Member - Research Associate

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+49 5251 60-6839
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ZM2.A.03-11
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This is Wei-Fan Chen's personal page.


Open list in Research Information System

2022

Analyzing Culture-Specific Argument Structures in Learner Essays

W. Chen, M. Chen, G. Mudgal, H. Wachsmuth, in: Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51 - 61


Investigating the argumentation structures of EFL learners from diverse language backgrounds

M. Chen, G. Mudgal, W. Chen, H. Wachsmuth. Investigating the argumentation structures of EFL learners from diverse language backgrounds. In: , 2022.


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. 224-223

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.


Controlled Neural Sentence-Level Reframing of News Articles

W. Chen, K. Al Khatib, B. Stein, H. Wachsmuth, in: Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 2683 - 2693


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


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


Abstractive Snippet Generation

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


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

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


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 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


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


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

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


Chinese Textual Sentiment Analysis: Datasets, Resources and Tools

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


2015

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



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


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


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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