doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
Song Feng, Hui Wan, Chulaka Gunasekara, Siva Patel, Sachindra Joshi, Luis Lastras
Abstract
We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents. Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that corresponds to higher-level relations across text sections as well as lower-level relations between discourse units within a section. Then we present these dialogue flows to crowd contributors to create conversational utterances. The dataset includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets, this dataset covers a variety of dialogue scenes in information-seeking conversations. For evaluating the versatility of the dataset, we introduce multiple dialogue modeling tasks and present baseline approaches.- Anthology ID:
- 2020.emnlp-main.652
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8118–8128
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.652
- DOI:
- 10.18653/v1/2020.emnlp-main.652
- Cite (ACL):
- Song Feng, Hui Wan, Chulaka Gunasekara, Siva Patel, Sachindra Joshi, and Luis Lastras. 2020. doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8118–8128, Online. Association for Computational Linguistics.
- Cite (Informal):
- doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset (Feng et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.652.pdf
- Code
- additional community code
- Data
- Doc2Dial, doc2dial, CoQA, DoQA, QuAC, SQuAD, ShARC