# _VivesDebate_: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament
## 5 Related Work: Other Computational Argumentation Corpora
At this point, it is possible to observe the strong dependence between the analysed corpora and the different tasks of computational argumentation. The _US2016_ debate corpus was presented in [32] as the largest argumentative corpus with great versatility between different aspects of argumentation such as discourse analysis, Argument Mining, and automatic Argument Analysis. The _US2016_ compiles the transcripts of the 2016 US presidential election TV debate and the subsequent online forum debate (i.e., Reddit). The text is analysed and divided into argument maps consisting of 500 to 1500 words. The annotation process is carried out independently for each argument map, where the text is segmented into ADUs and argumentative relations between ADUs are identified. The final corpus has 97,999 words, with a sub-corpus of 58,900 words from the TV debates and 39,099 words from the Reddit discussion. Despite the improvement achieved with this new corpus, we still identify two major issues that may hinder the performance of the trained models and the scope of the experiments: the quality of the uttered arguments, and the traceability of discourse. Electoral campaigns and debates are usually focused on reaching the majority of voters rather than properly using arguments, or having a rational debate. Furthermore, the arguments retrieved from an online forum might neither be of the ideal quality. Thus, the trained models using this data can be biased in a way that does not reflect the reality of a more rational and logical argumentation. Finally, the most recent argumentative corpus was presented in [39]. The authors present the ReCAP corpus of monologue argument graphs extracted from German education politics. More than 100 argument graphs are annotated from natural language text sources like party press releases and parliamentary motions. This corpus annotates the ADU segments identified in the text and relations between different ADUs (i.e., inferences). Furthermore, the authors have also included annotations of the underlying reasoning pattern (i.e., argumentation schemes) of arguments. These are not the unique resources published in the literature for computational argumentation research. Many research done in Argument Mining includesnew corpora, for the healthcare domain in [40], for legal argumentation in [41], and for online social network analysis in [42] among other different domains. However, for our comparison, we have focused on the most used corpora in computational argumentation research, and corpora created from a more generalist perspective. A comparison of the previously analysed corpora is presented in Table 5. Furthermore, we have added the _VivesDebate_ corpus to the comparison in order to provide a reference to understand the significance of our contribution. Seven different features that we consider indicators of the quality of a corpus have been analysed in our comparison. First, the format of the argumentative data indicates if the arguments are retrieved from a monologue (M) or a dialogue (D). Furthermore, it is also important to know the source of the arguments, if they come from a text source (T) or from a speech transcript (S). The domain indicates the context from which the corpus has been created (e.g., competitive debate, online forum, etc.). This is a key feature to determine the quality of the arguments contained in the resulting corpus, since major linguistic aspects, such as the richness of vocabulary or the originality of arguments, will be significantly different from one domain to another. The tasks feature indicates in which argumentative tasks a corpus can be useful: Argument Mining (AM), Argument Analysis (AA) and representation, Argument Evaluation (AE), Argument Generation (AG), and Argument Summarisation (AS). This feature is important to observe the versatility of each analysed corpus. The language indicates if a corpus is available in English (EN), German (DE), or Catalan (CAT).