# _VivesDebate_: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament
## 5 Related Work: Other Computational Argumentation Corpora
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**File** & **Words** & **ADUs** & **Conflicts** & **Inferences** & **Rephrases** & **Score F** & **Score A** \\ \hline _Debate14.csv_ & 4790 & 302 & 74 & 400 & 47 & 3.83 & 3.80 \\ _Debate15.csv_ & 4550 & 173 & 23 & 113 & 13 & 3.95 & 3.94 \\ _Debate16.csv_ & 4887 & 288 & 94 & 639 & 53 & 3.33 & 3.39 \\ _Debate17.csv_ & 3891 & 164 & 8 & 123 & 8 & 3.00 & 3.26 \\ _Debate18.csv_ & 3701 & 166 & 6 & 149 & 4 & 2.80 & 2.77 \\ _Debate19.csv_ & 4645 & 186 & 13 & 159 & 1 & 4.24 & 4.34 \\ _Debate20.csv_ & 5484 & 306 & 33 & 1306 & 55 & 3.53 & 3.49 \\ _Debate21.csv_ & 5064 & 278 & 102 & 1076 & 42 & 3.17 & 3.18 \\ _Debate22.csv_ & 4669 & 330 & 16 & 408 & 1 & 4.40 & 4.22 \\ _Debate23.csv_ & 4420 & 266 & 136 & 917 & 26 & 2.74 & 2.69 \\ _Debate24.csv_ & 5139 & 267 & 380 & 1002 & 39 & 4.41 & 4.37 \\ _Debate25.csv_ & 4828 & 321 & 7 & 337 & 0 & 4.09 & 3.88 \\ _Debate26.csv_ & 4440 & 290 & 16 & 328 & 5 & 4.16 & 3.93 \\ _Debate27.csv_ & 5012 & 234 & 106 & 645 & 24 & 3.49 & 2.33 \\ _Debate28.csv_ & 4254 & 310 & 21 & 344 & 2 & 2.75 & 2.36 \\ _Debate29.csv_ & 5889 & 337 & 51 & 1203 & 72 & - & - \\ \hline _VivesDebate_ & 139,756 & 7810 & 1558 & 12,653 & 747 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._forum are annotated with their argumentative structures, capturing the _strength_ of the identified arguments. In the CDCP corpus, each comment is segmented into elementary units (i.e., Facts, Testimony, Value, Policy and Reference). Support relations between these elementary units are annotated in order to provide structural information. The authors defined the evaluability of an argument for those cases in which all the propositions that make up this argument are supported by an explicit premise of the same type of elementary unit. The _strength_ of an argument is measured by comparing the type of the elementary units that comprise it. Another important part of computational argumentation, which was not approachable from the reviewed corpora, is the automatic generation of natural language arguments. This is a recent research topic, which requires an important amount of data to achieve competitive results. In [37], the authors present a new annotated corpus aimed at approaching this task. The GPR-KB-55 contains 200 speeches from a debate competition that were analysed, each one debating one of the 50 different topics existing in these speeches. The resulting corpus consists of 12,431 argument pairs containing a claim and its rebuttal with annotations regarding the relevance of a claim to its motion, the stance of the claim, and its appearance in a piece of speech (i.e., mentioned/not mentioned, explicit/implicit). Even though some linguistic annotations were done, the argument structure or the flow of discourse were not annotated in the GPR-KB-55 corpus. This corpus is part of the IBM Project Debater ([[https://www.research.ibm.com/artificial-intelligence/project-debater/](https://www.research.ibm.com/artificial-intelligence/project-debater/)]([https://www.research.ibm.com/artificial-intelligence/project-debater/](https://www.research.ibm.com/artificial-intelligence/project-debater/)), accessed on 2 August 2021), which encompasses a large set of different corpora, each one aimed at a specific task of the argumentative process. A different perspective on natural language Argument Generation is presented in [38], where the authors provide a new corpus aimed at the word-level summarisation of arguments. The DebateSum corpus consists of 187,386 debate summaries without any structural annotation, retrieved from the debate tournaments organised by the National Speech and Debate Association. The only annotation provided by this corpus is the segmentation of arguments-evidences-summary triplets extracted directly from the transcripts. Again, the usefulness of this resource remains strongly linked to the specific tasks of argument summarisation and language modelling.