Abstract
This paper presents the problem of conversational plotting agents that carry out plotting actions from natural language instructions. To facilitate the development of such agents, we introduce ChartDialogs, a new multi-turn dialog dataset, covering a popular plotting library, matplotlib. The dataset contains over 15,000 dialog turns from 3,200 dialogs covering the majority of matplotlib plot types. Extensive experiments show the best-performing method achieving 61% plotting accuracy, demonstrating that the dataset presents a non-trivial challenge for future research on this task.- Anthology ID:
- 2020.acl-main.328
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3559–3574
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.328
- DOI:
- 10.18653/v1/2020.acl-main.328
- Cite (ACL):
- Yutong Shao and Ndapa Nakashole. 2020. ChartDialogs: Plotting from Natural Language Instructions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3559–3574, Online. Association for Computational Linguistics.
- Cite (Informal):
- ChartDialogs: Plotting from Natural Language Instructions (Shao & Nakashole, ACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.acl-main.328.pdf