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首页> 外文会议>Annual meeting of the Association for Computational Linguistics >Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
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Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction

机译：确定科学排行榜构建的任务，数据集，评估指标和数字分数

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摘要

While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.
机译：尽管快速启动新任务和新数据集有助于促进社区朝着有趣方向发展积极的研究，但要跟踪不同数据集上不同领域的大量研究活动可能会变得越来越困难。社区可以从能够总结科学结果的自动系统中受益匪浅，例如以排行榜的形式。在本文中，我们建立了两个数据集，并开发了一个框架（TDMS-IE），旨在自动从NLP论文中提取任务，数据集，指标和分数，从而自动建立排行榜。实验表明，我们的模型大大优于几个基准。我们的模型是例如在NLP域中自动排行榜构建的第一步。

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