Aman Berhe


2022

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Bazinga! A Dataset for Multi-Party Dialogues Structuring
Paul Lerner | Juliette Bergoënd | Camille Guinaudeau | Hervé Bredin | Benjamin Maurice | Sharleyne Lefevre | Martin Bouteiller | Aman Berhe | Léo Galmant | Ruiqing Yin | Claude Barras
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We introduce a dataset built around a large collection of TV (and movie) series. Those are filled with challenging multi-party dialogues. Moreover, TV series come with a very active fan base that allows the collection of metadata and accelerates annotation. With 16 TV and movie series, Bazinga! amounts to 400+ hours of speech and 8M+ tokens, including 500K+ tokens annotated with the speaker, addressee, and entity linking information. Along with the dataset, we also provide a baseline for speaker diarization, punctuation restoration, and person entity recognition. The results demonstrate the difficulty of the tasks and of transfer learning from models trained on mono-speaker audio or written text, which is more widely available. This work is a step towards better multi-party dialogue structuring and understanding. Bazinga! is available at hf.co/bazinga. Because (a large) part of Bazinga! is only partially annotated, we also expect this dataset to foster research towards self- or weakly-supervised learning methods.