A Recorded Debating Dataset

A Recorded Debating Dataset Shachar Mirkin1, Michal Jacovi1, Tamar Lavee1,2, Hong-Kwang Kuo2, Samuel Thomas2, Leslie Sager3, Lili Kotlerman1, Elad Venezian1, Noam Slonim1 1IBM Research – Haifa, Israel, 2IBM Watson – Yorktown Heights, New York, USA, 3Tadiad, Tel Aviv, Israel {shacharm,michal.jacovi,lili.kotlerman,eladv,noams}@il.ibm.com, tamar.lavee1@ibm.com, {hkuo,sthomas}@us.ibm.com,sagerleslie@gmail.com Abstract This paper describes an English audio and textual dataset of debating speeches, a unique resource for the growing research field of computational argumentation and debating technologies. We detail the process of speech recording by professional debaters, the transcription of the speeches with an Automatic Speech Recognition (ASR) system, their consequent automatic processing to produce a text that is more “NLP-friendly”, and in parallel – the manual transcription of the speeches in order to produce gold-standard “reference” transcripts. We release 60 speeches on various controversial topics, each in five formats corresponding to the different stages in the production of the data. The intention is to allow utilizing this resource for multiple research purposes, be it the addition of in-domain training data for a debate-specific ASR system, or applying argumentation mining on either noisy or clean debate transcripts. We intend to make further releases of this data in the future. Keywords: debating technologies, computational argumentation, argumentation mining, automatic speech recognition 1. Introduction Computational argumentation and debating technologies aim to automate the extraction, understanding and gene- ration of argumentative discourse. This field has seen a surge in research in recent years, and involves a variety of tasks, over various domains, including legal, scientific wri- ting and education. Much of the focus is on argumentation mining, the detection of arguments in text and their classi- fication (Palau and Moens, 2009), but many other tasks are being addressed as well, including argument stance classifi- cation (Sobhani et al., 2015; Bar-Haim et al., 2017), the au- tomatic generation of arguments (Bilu and Slonim, 2016), identification of persuasive arguments (Wei et al., 2016), quality assessment (Wachsmuth et al., 2017a) and more. Multiple datasets are available for such research, mostly in English, such as the Internet Argument Corpus (Walker et al., 2012), that consists of numerous annotated political discussions in internet forums, ArgRewrite (Zhang et al., 2017), a corpus of argumentative essay revisions, and the datasets released by IBM Research as part of the Debater Project (Rinott et al., 2015; Aharoni et al., 2014). Lippi and Torroni (2016) list several additional such datasets. Furt- her, Wachsmuth et al. (2017b) have released an argument search engine over multiple debating websites, and Aker and Zhang (2017) have initiated the projection of some da- tasets to languages other than English, such as Chinese. All of the above are based on written texts, while datasets of spoken debates, outside of the political domain, are scarce. A spoken debate differs from a written essay or discussion not only in structure and content, but also in style as in any other case of spoken vs. written language. Zhang et al. (2016) made available transcripts from the Intelligence Squared1 debating television show2. The transcripts of the show are available on the show’s …

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