The Gutenberg Dialogue Dataset

Richard Csaky, Gábor Recski


Abstract
Large datasets are essential for neural modeling of many NLP tasks. Current publicly available open-domain dialogue datasets offer a trade-off between quality (e.g., DailyDialog) and size (e.g., Opensubtitles). We narrow this gap by building a high-quality dataset of 14.8M utterances in English, and smaller datasets in German, Dutch, Spanish, Portuguese, Italian, and Hungarian. We extract and process dialogues from public-domain books made available by Project Gutenberg. We describe our dialogue extraction pipeline, analyze the effects of the various heuristics used, and present an error analysis of extracted dialogues. Finally, we conduct experiments showing that better response quality can be achieved in zero-shot and finetuning settings by training on our data than on the larger but much noisier Opensubtitles dataset. Our open-source pipeline (https://github.com/ricsinaruto/gutenberg-dialog) can be extended to further languages with little additional effort. Researchers can also build their versions of existing datasets by adjusting various trade-off parameters.
Anthology ID:
2021.eacl-main.11
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
138–159
Language:
URL:
https://aclanthology.org/2021.eacl-main.11
DOI:
10.18653/v1/2021.eacl-main.11
Bibkey:
Cite (ACL):
Richard Csaky and Gábor Recski. 2021. The Gutenberg Dialogue Dataset. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 138–159, Online. Association for Computational Linguistics.
Cite (Informal):
The Gutenberg Dialogue Dataset (Csaky & Recski, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.11.pdf
Code
 ricsinaruto/gutenberg-dialog
Data
Gutenberg Dialog DatasetDailyDialog