AxCell: Automatic Extraction of Results from Machine Learning Papers

Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel, Ross Taylor, Robert Stojnic


Abstract
Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers. AxCell uses several novel components, including a table segmentation subtask, to learn relevant structural knowledge that aids extraction. When compared with existing methods, our approach significantly improves the state of the art for results extraction. We also release a structured, annotated dataset for training models for results extraction, and a dataset for evaluating the performance of models on this task. Lastly, we show the viability of our approach enables it to be used for semi-automated results extraction in production, suggesting our improvements make this task practically viable for the first time. Code is available on GitHub.
Anthology ID:
2020.emnlp-main.692
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8580–8594
Language:
URL:
https://aclanthology.org/2020.emnlp-main.692
DOI:
10.18653/v1/2020.emnlp-main.692
Bibkey:
Cite (ACL):
Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel, Ross Taylor, and Robert Stojnic. 2020. AxCell: Automatic Extraction of Results from Machine Learning Papers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8580–8594, Online. Association for Computational Linguistics.
Cite (Informal):
AxCell: Automatic Extraction of Results from Machine Learning Papers (Kardas et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.692.pdf
Video:
 https://slideslive.com/38938992
Code
 paperswithcode/axcell
Data
ArxivPapersLinkedResultsPWC LeaderboardsSegmentedTablesSST-2