Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
| Challenge: | Existing research in computational argumentation and debating technologies focuses on argumentation mining, but other tasks are being addressed as well. |
| Approach: | They describe a dataset of debating speeches in English that is used for research . they use an automatic speech recognition system to produce a more "nLP-friendly" text . |
| Outcome: | The proposed dataset contains 60 speeches on various controversial topics, each in five formats corresponding to different stages in production. |
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| Challenge: | Recent advances in deep learning have improved the performance of abstractive summarization systems. |
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IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)
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| Challenge: | Argument mining (AM) is a computational process that is used to analyze information in a debating system. |
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Advances in Debating Technologies: Building AI That Can Debate Humans (2021.acl-tutorials)
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| Challenge: | This tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as "computational technologies developed directly to enhance, support, and engage with human debating" the tutorial provides a holistic view of a debated system, and discusses practical applications and future challenges of debation technologies. |
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Listening Comprehension over Argumentative Content (D18-1)
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Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
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Out of the Echo Chamber: Detecting Countering Debate Speeches (2020.acl-main)
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| Challenge: | Existing algorithms to detect articles that counter the arguments in debate speeches are unsuccessful, suggesting room for further research. |
| Approach: | They propose a task to detect articles that counter the arguments made in debate speeches by annotating them from a dataset of 3,685 such speeches. |
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ParlVote: A Corpus for Sentiment Analysis of Political Debates (2020.lrec-1)
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| Challenge: | Debate transcripts from the UK Parliament contain information about the positions taken by politicians towards important topics, but are difficult for humans to process manually. |
| Approach: | They propose to use a linear classifier and a transformer word embedding model to classify sentiment polarity in debate speeches to evaluate sentiment analysis systems for the political domain. |
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Ann Clifton, Sravana Reddy, Yongze Yu, Aasish Pappu, Rezvaneh Rezapour, Hamed Bonab, Maria Eskevich, Gareth Jones, Jussi Karlgren, Ben Carterette, Rosie Jones
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Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
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The Discussion Tracker Corpus of Collaborative Argumentation (2020.lrec-1)
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| Challenge: | The Discussion Tracker corpus is an annotated dataset of transcripts of spoken, multi-party argumentation transcribed from 985 minutes of audio . |
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Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates (P19-1)
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| Challenge: | Political debates are a natural application scenario for Argument Mining. |
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