论文标题
1cademy @ Causal News Corpus 2022：通过基于梁搜索的位置选择器增强因果跨度检测
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector
论文作者
论文摘要
在本文中，我们介绍了2022年共享任务3〜 \ cite {tan-etal-2022-event}的因果信号跨度检测的方法和经验观察。我们将任务建模为阅读理解（RC）问题，并将基于令牌级RC的跨度预测范式应用于任务作为基线。我们探索不同的培训目标，以微调模型，以及基于语言模型（LM）的数据增强（DA）技巧，以改善性能。此外，我们提出了有效的横梁 - 搜索后处理策略，以获得跨度检测的缺点，以获得进一步的性能增益。我们的方法在案例竞赛中平均达到54.15的$ f_1 $得分为54.15，并排名\ textbf {$ 1^{st} $}。我们的代码可在\ url {https://github.com/gzhang-umich/1cademyteamofcase}中获得。
In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection -- Subtask 2 of Shared task 3~\cite{tan-etal-2022-event} at CASE 2022. The shared task aims to extract the cause, effect, and signal spans from a given causal sentence. We model the task as a reading comprehension (RC) problem and apply a token-level RC-based span prediction paradigm to the task as the baseline. We explore different training objectives to fine-tune the model, as well as data augmentation (DA) tricks based on the language model (LM) for performance improvement. Additionally, we propose an efficient beam-search post-processing strategy to due with the drawbacks of span detection to obtain a further performance gain. Our approach achieves an average $F_1$ score of 54.15 and ranks \textbf{$1^{st}$} in the CASE competition. Our code is available at \url{https://github.com/Gzhang-umich/1CademyTeamOfCASE}.
