@inproceedings{shao-nakashole-2020-chartdialogs,
title = "{C}hart{D}ialogs: {P}lotting from {N}atural {L}anguage {I}nstructions",
author = "Shao, Yutong and
Nakashole, Ndapa",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.328/",
doi = "10.18653/v1/2020.acl-main.328",
pages = "3559--3574",
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."
}
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%0 Conference Proceedings
%T ChartDialogs: Plotting from Natural Language Instructions
%A Shao, Yutong
%A Nakashole, Ndapa
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F shao-nakashole-2020-chartdialogs
%X 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.
%R 10.18653/v1/2020.acl-main.328
%U https://aclanthology.org/2020.acl-main.328/
%U https://doi.org/10.18653/v1/2020.acl-main.328
%P 3559-3574
Markdown (Informal)
[ChartDialogs: Plotting from Natural Language Instructions](https://aclanthology.org/2020.acl-main.328/) (Shao & Nakashole, ACL 2020)
ACL