Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.

本研究旨在解决在科技论文中理解表格结构与表格内容，主要通过提供一个包含这些信息的数据集和SemEval任务挑战赛中两个子任务的方式来实现，共有69个团队参与，19个成功提交了子任务A的结果，12个成功提交了子任务B的结果。

SemEval-2021任务9：科学文档中表格数据的事实验证和证据搜索（SEM-TAB-FACTS）