1 code implementation • ArgMining (ACL) 2022 • Wei-Fan Chen, Mei-Hua Chen, Garima Mudgal, Henning Wachsmuth
Based on the ICLE corpus containing essays written by English learners of 16 different mother tongues, we train natural language processing models to mine argumentative discourse units (ADUs) as well as to assess the essays’ quality in terms of organization and argument strength.
no code implementations • INLG (ACL) 2020 • Shahbaz Syed, Wei-Fan Chen, Matthias Hagen, Benno Stein, Henning Wachsmuth, Martin Potthast
We propose a shared task on abstractive snippet generation for web pages, a novel task of generating query-biased abstractive summaries for documents that are to be shown on a search results page.
no code implementations • Findings (EMNLP) 2021 • Wei-Fan Chen, Khalid Al-Khatib, Benno Stein, Henning Wachsmuth
Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques.
1 code implementation • EACL 2021 • Milad Alshomary, Wei-Fan Chen, Timon Gurcke, Henning Wachsmuth
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.
1 code implementation • EMNLP (NLP+CSS) 2020 • Wei-Fan Chen, Khalid Al-Khatib, Henning Wachsmuth, Benno Stein
Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Wei-Fan Chen, Khalid Al-Khatib, Benno Stein, Henning Wachsmuth
In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model.
1 code implementation • 25 Feb 2020 • Wei-Fan Chen, Shahbaz Syed, Benno Stein, Matthias Hagen, Martin Potthast
An abstractive snippet is an originally created piece of text to summarize a web page on a search engine results page.
Ranked #1 on
Text Summarization
on Webis-Snippet-20 Corpus
no code implementations • WS 2019 • Wei-Fan Chen, Khalid Al Khatib, Matthias Hagen, Henning Wachsmuth, Benno Stein
Many discussions on online platforms suffer from users offending others by using abusive terminology, threatening each other, or being sarcastic.
no code implementations • WS 2018 • Wei-Fan Chen, Henning Wachsmuth, Khalid Al-Khatib, Benno Stein
This paper introduces the task of {``}flipping{''} the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias.
1 code implementation • WS 2017 • Yamen Ajjour, Wei-Fan Chen, Johannes Kiesel, Henning Wachsmuth, Benno Stein
The segmentation of an argumentative text into argument units and their non-argumentative counterparts is the first step in identifying the argumentative structure of the text.
no code implementations • COLING 2016 • Wei-Fan Chen, Fang-Yu Lin, Lun-Wei Ku
This paper presents WordForce, a system powered by the state of the art neural network model to visualize the learned user-dependent word embeddings from each post according to the post content and its engaged users.
no code implementations • COLING 2016 • Wei-Fan Chen, Lun-Wei Ku
Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0. 755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld.
no code implementations • COLING 2016 • Lun-Wei Ku, Wei-Fan Chen
The basic processing tools are from CKIP Participants can download these resources, use them and solve the problems they encounter in this tutorial.
no code implementations • 11 Nov 2016 • Wei-Fan Chen, Lun-Wei Ku
Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0. 755 macro-average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld.