@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{16868, author = {{Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)}}, location = {{Seattle, USA}}, pages = {{4334--4345}}, publisher = {{Association for Computational Linguistics}}, title = {{{Target Inference in Argument Conclusion Generation}}}, year = {{2020}}, } @inproceedings{20141, author = {{Heindorf, Stefan and Scholten, Yan and Wachsmuth, Henning and Ngonga Ngomo, Axel-Cyrille and Potthast, Martin}}, booktitle = {{Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020)}}, pages = {{3023--3030}}, title = {{{CauseNet: Towards a Causality Graph Extracted from the Web}}}, doi = {{10.1145/3340531.3412763}}, year = {{2020}}, } @inproceedings{21177, abstract = {{Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new state-of-the-art results. A thorough evaluation of the attention mechanism for the task of Argumentation Mining is missing. With this paper, we report a comparative evaluation of attention layers in combination with a bidirectional long short-term memory network, which is the current state-of-the-art approach for the unit segmentation task. We also compare sentence-level contextualized word embeddings to pre-generated ones. Our findings suggest that for this task, the additional attention layer does not improve the performance. In most cases, contextualized embeddings do also not show an improvement on the score achieved by pre-defined embeddings.}}, author = {{Spliethöver, Maximilian and Klaff, Jonas and Heuer, Hendrik}}, booktitle = {{Proceedings of the 6th Workshop on Argument Mining}}, location = {{Florence, Italy}}, pages = {{74--82}}, publisher = {{Association for Computational Linguistics}}, title = {{{Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation}}}, doi = {{10.18653/v1/W19-4509}}, year = {{2019}}, } @techreport{16847, abstract = {{In this work we describe our results achieved in the ProtestNews Lab at CLEF 2019. To tackle the problems of event sentence detection and event extraction we decided to use contextualized string embeddings. The models were trained on a data corpus collected from Indian news sources, but evaluated on data obtained from news sources from other countries as well, such as China. Our models have obtained competitive results and have scored 3rd in the event sentence detection task and 1st in the event extraction task based on average F1-scores for different test datasets.}}, author = {{Skitalinskaya, Gabriella and Klaff, Jonas and Spliethöver, Maximilian}}, pages = {{7}}, title = {{{CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction}}}, volume = {{2380}}, year = {{2019}}, } @inproceedings{11709, author = {{Potthast, Martin and Gienapp, Lukas and Euchner, Florian and Heilenkötter, Nick and Weidmann, Nico and Wachsmuth, Henning and Stein, Benno and Hagen, Matthias}}, booktitle = {{42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019)}}, pages = {{1117 -- 1120}}, publisher = {{ACM}}, title = {{{Argument Search: Assessing Argument Relevance}}}, doi = {{10.1145/3331184.3331327}}, year = {{2019}}, } @misc{11713, author = {{Wachsmuth, Henning}}, booktitle = {{Computational Linguistics}}, number = {{3}}, pages = {{603 -- 606}}, publisher = {{ACL}}, title = {{{Book Review: Argumentation Mining}}}, volume = {{45}}, year = {{2019}}, } @inproceedings{11714, author = {{Ajjour, Yamen and Wachsmuth, Henning and Kiesel, Johannes and Potthast, Martin and Hagen, Matthias and Stein, Benno}}, booktitle = {{Proceedings of the 42nd Edition of the German Conference on Artificial Intelligence}}, pages = {{48--59}}, title = {{{Data Acquisition for Argument Search: The args.me Corpus}}}, year = {{2019}}, } @inproceedings{12931, author = {{Ajjour, Yamen and Alshomary, Milad and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing}}, pages = {{2915 -- 2925}}, title = {{{Modeling Frames in Argumentation}}}, year = {{2019}}, } @proceedings{15235, editor = {{Stein, Benno and Wachsmuth, Henning}}, publisher = {{Association for Computational Linguistics}}, title = {{{Proceedings of the 6th Workshop on Argument Mining}}}, year = {{2019}}, } @inproceedings{13144, author = {{El Baff, Roxanne and Wachsmuth, Henning and Al-Khatib, Khalid and Stede, Manfred and Stein, Benno}}, booktitle = {{Proceedings of the 12th International Conference on Natural Language Generation}}, location = {{Tokyo, Japan}}, pages = {{54--64}}, publisher = {{Association for Computational Linguistics}}, title = {{{Computational Argumentation Synthesis as a Language Modeling Task}}}, year = {{2019}}, } @inproceedings{10284, abstract = {{We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample of the Common Crawl). To discover reuse beyond verbatim copy and paste, we employ state-of-the-art text reuse detection technology, scaling it for the first time to process the entire Wikipedia as part of a distributed retrieval pipeline. We further report on a pilot analysis of the 100 million reuse cases inside, and the 1.6 million reuse cases outside Wikipedia that we discovered. Text reuse inside Wikipedia gives rise to new tasks such as article template induction, fixing quality flaws, or complementing Wikipedia's ontology. Text reuse outside Wikipedia yields a tangible metric for the emerging field of quantifying Wikipedia's influence on the web. To foster future research into these tasks, and for reproducibility's sake, the Wikipedia text reuse corpus and the retrieval pipeline are made freely available.}}, author = {{Alshomary, Milad and Völske, Michael and Licht, Tristan and Wachsmuth, Henning and Stein, Benno and Hagen, Matthias and Potthast, Martin}}, booktitle = {{Advances in Information Retrieval}}, editor = {{Azzopardi, Leif and Stein, Benno and Fuhr, Norbert and Mayr, Philipp and Hauff, Claudia and Hiemstra, Djoerd}}, isbn = {{978-3-030-15712-8}}, pages = {{747--754}}, publisher = {{Springer International Publishing}}, title = {{{Wikipedia Text Reuse: Within and Without}}}, year = {{2019}}, } @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}}, } @inproceedings{20188, author = {{Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno}}, booktitle = {{Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}}, pages = {{1930–1940}}, title = {{{The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants}}}, year = {{2018}}, } @inproceedings{3804, author = {{Al Khatib, Khalid and Wachsmuth, Henning and Lang, Kevin and Herpel, Jakob and Hagen, Matthias and Stein, Benno}}, booktitle = {{Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}}, pages = {{2545--2555}}, title = {{{Modeling Deliberative Argumentation Strategies on Wikipedia}}}, year = {{2018}}, } @inproceedings{3806, author = {{Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno}}, booktitle = {{Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)}}, pages = {{386--396}}, title = {{{Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation}}}, year = {{2018}}, } @inproceedings{3807, author = {{Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno}}, booktitle = {{Proceedings of The 12th International Workshop on Semantic Evaluation}}, pages = {{763--772}}, title = {{{SemEval-2018 Task 12: The Argument Reasoning Comprehension Task}}}, year = {{2018}}, } @inproceedings{3821, author = {{Wachsmuth, Henning and Syed, Shahbaz and Stein, Benno}}, booktitle = {{Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}}, pages = {{241--251}}, title = {{{Retrieval of the Best Counterargument without Prior Topic Knowledge}}}, year = {{2018}}, } @inproceedings{21173, author = {{Bonfert, Michael and Spliethöver, Maximilian and Arzaroli, Roman and Lange, Marvin and Hanci, Martin and Porzel, Robert}}, booktitle = {{Proceedings of the 20th ACM International Conference on Multimodal Interaction}}, isbn = {{9781450356923}}, title = {{{If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands}}}, doi = {{10.1145/3242969.3242995}}, year = {{2018}}, }