@inbook{45882, author = {{Bäumer, Frederik Simon and Chen, Wei-Fan and Geierhos, Michaela and Kersting, Joschka and Wachsmuth, Henning}}, booktitle = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}, editor = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{65--84}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation}}}, doi = {{10.5281/zenodo.8068456}}, volume = {{412}}, year = {{2023}}, } @inproceedings{33274, author = {{Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)}}, pages = {{51 -- 61}}, title = {{{Analyzing Culture-Specific Argument Structures in Learner Essays}}}, year = {{2022}}, } @inproceedings{31068, author = {{Chen, Mei-Hua and Mudgal, Garima and Chen, Wei-Fan and Wachsmuth, Henning}}, booktitle = {{EUROCALL}}, title = {{{Investigating the argumentation structures of EFL learners from diverse language backgrounds}}}, year = {{2022}}, } @inproceedings{21178, 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.}}, author = {{Alshomary, Milad and Chen, Wei-Fan and Gurcke, Timon and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}}, location = {{Online}}, pages = {{224--223}}, publisher = {{Association for Computational Linguistics}}, title = {{{Belief-based Generation of Argumentative Claims}}}, year = {{2021}}, } @inproceedings{23709, author = {{Chen, Wei-Fan and Al Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2021}}, pages = {{2683 -- 2693}}, title = {{{Controlled Neural Sentence-Level Reframing of News Articles}}}, year = {{2021}}, } @inproceedings{3776, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science}}, pages = {{149--154}}, title = {{{Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity}}}, year = {{2020}}, } @inproceedings{20137, author = {{Syed, Shahbaz and Chen, Wei-Fan and Hagen, Matthias and Stein, Benno and Wachsmuth, Henning and Potthast, Martin}}, booktitle = {{Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020)}}, pages = {{237--241}}, title = {{{Task Proposal: Abstractive Snippet Generation for Web Pages}}}, year = {{2020}}, } @inproceedings{3818, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2020}}, pages = {{4290--4300}}, title = {{{Detecting Media Bias in News Articles using Gaussian Bias Distributions}}}, year = {{2020}}, } @inproceedings{15826, author = {{Chen, Wei-Fan and Syed, Shahbaz and Stein, Benno and Hagen, Matthias and Potthast, Martin}}, booktitle = {{Proceedings of the Web Conference 2020}}, pages = {{1309--1319}}, title = {{{Abstractive Snippet Generation}}}, year = {{2020}}, } @inproceedings{13259, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Hagen, Matthias and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom}}, pages = {{76--82}}, title = {{{Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition}}}, year = {{2019}}, } @article{14886, author = {{Chen, Wei-Fan and Ku, Lun-Wei}}, journal = {{IEEE Transactions on Knowledge and Data Engineering}}, number = {{10}}, pages = {{2013--2023}}, publisher = {{IEEE}}, title = {{{We Like, We Post: A Joint User-Post Approach for Facebook Post Stance Labeling}}}, volume = {{30}}, year = {{2018}}, } @article{14887, author = {{Chen, Mei-Hua and Chen, Wei-Fan and Ku, Lun-Wei}}, journal = {{IEEE Access}}, pages = {{24433--24442}}, publisher = {{IEEE}}, title = {{{Application of Sentiment Analysis to Language Learning}}}, volume = {{6}}, year = {{2018}}, } @article{14888, author = {{Chen, Wei-Fan and Ku, Lun-Wei}}, journal = {{圖書館學與資訊科學}}, publisher = {{國立台灣師範大學圖書資訊學研究所}}, title = {{{中文情感語意分析套件 CSentiPackage 發展與應用}}}, year = {{2018}}, } @inproceedings{11710, author = {{Chen, Wei-Fan and Wachsmuth, Henning and Al Khatib, Khalid and Stein, Benno}}, booktitle = {{Proceedings of the 11th International Conference on Natural Language Generation}}, pages = {{79--88}}, publisher = {{Association for Computational Linguistics}}, title = {{{Learning to Flip the Bias of News Headlines}}}, year = {{2018}}, } @inproceedings{14873, author = {{Chen, Wei-Fan and Hagen, Matthias and Stein, Benno and Potthast, Martin}}, booktitle = {{Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval}}, pages = {{1033--1036}}, title = {{{A User Study on Snippet Generation: Text Reuse vs. Paraphrases}}}, year = {{2018}}, } @inproceedings{14885, author = {{Potthast, Martin and Chen, Wei-Fan and Hagen, Matthias and Stein, Benno}}, booktitle = {{Proceedings of the Second International Workshop on Recent Trends in News Information Retrieval}}, pages = {{3--5}}, title = {{{A Plan for Ancillary Copyright: Original Snippets.}}}, year = {{2018}}, } @inproceedings{3751, author = {{Ajjour, Yamen and Chen, Wei-Fan and Kiesel, Johannes and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 4th Workshop on Argument Mining}}, pages = {{118--128}}, title = {{{Unit Segmentation of Argumentative Texts}}}, year = {{2017}}, } @inproceedings{14884, author = {{Chen, Wei-Fan and Chen, Yi-Pei and Ku, Lun-Wei}}, booktitle = {{International Conference on HCI in Business, Government, and Organizations}}, pages = {{190--202}}, title = {{{How to Get Endorsements? Predicting Facebook Likes Using Post Content and User Engagement}}}, year = {{2017}}, } @inproceedings{14881, author = {{Chen, Wei-Fan and Ku, Lun-Wei}}, booktitle = {{Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics}}, pages = {{1635--1645}}, title = {{{UTCNN: a Deep Learning Model of Stance Classification on Social Media Text}}}, year = {{2016}}, } @inproceedings{14882, author = {{Chen, Wei-Fan and Lin, Fang-Yu and Ku, Lun-Wei}}, booktitle = {{Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations}}, pages = {{273--277}}, title = {{{WordForce: Visualizing Controversial Words in Debates}}}, year = {{2016}}, }