DEBACER: a method for slicing moderated debates
This addresses the need for better analysis of political and legal debates, though it is incremental as it builds on existing NLP methods.
The paper tackles the problem of automatically partitioning moderated debates into subject blocks by focusing on moderator behavior, and demonstrates DEBACER's effectiveness on minutes from the Assembly of the Republic of Portugal.
Subjects change frequently in moderated debates with several participants, such as in parliamentary sessions, electoral debates, and trials. Partitioning a debate into blocks with the same subject is essential for understanding. Often a moderator is responsible for defining when a new block begins so that the task of automatically partitioning a moderated debate can focus solely on the moderator's behavior. In this paper, we (i) propose a new algorithm, DEBACER, which partitions moderated debates; (ii) carry out a comparative study between conventional and BERTimbau pipelines; and (iii) validate DEBACER applying it to the minutes of the Assembly of the Republic of Portugal. Our results show the effectiveness of DEBACER. Keywords: Natural Language Processing, Political Documents, Spoken Text Processing, Speech Split, Dialogue Partitioning.