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


Abstract
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.
Anthology ID:
2022.lrec-1.367
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3434–3441
Language:
URL:
https://aclanthology.org/2022.lrec-1.367
DOI:
Bibkey:
Cite (ACL):
Paul Lerner, Juliette Bergoënd, Camille Guinaudeau, Hervé Bredin, Benjamin Maurice, Sharleyne Lefevre, Martin Bouteiller, Aman Berhe, Léo Galmant, Ruiqing Yin, and Claude Barras. 2022. Bazinga! A Dataset for Multi-Party Dialogues Structuring. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3434–3441, Marseille, France. European Language Resources Association.
Cite (Informal):
Bazinga! A Dataset for Multi-Party Dialogues Structuring (Lerner et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.367.pdf
Data
Serial Speakers