文章基本信息

标题：Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers
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作者：Lya Hulliyyatus Suadaa ; Hidetaka Kamigaito ; Manabu Okumura 等
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：3062-3071
DOI：10.18653/v1/2021.eacl-main.267
语种：English
出版社：ACL Anthology
摘要：Numerical tables are widely used to present experimental results in scientific papers. For table understanding, a metric-type is essential to discriminate numbers in the tables. We introduce a new information extraction task, metric-type identification from multi-level header numerical tables, and provide a dataset extracted from scientific papers consisting of header tables, captions, and metric-types. We then propose two joint-learning neural classification and generation schemes featuring pointer-generator-based and BERT-based models. Our results show that the joint models can handle both in-header and out-of-header metric-type identification problems.