Tables are widely used in various kinds of documents to present information
concisely. Understanding tables is a challenging problem that requires an
understanding of language and table structure, along with numerical and logical
reasoning. In this paper, we present our systems to solve Task 9 of
SemEval-2021: Statement Verification and Evidence Finding with Tables
(SEM-TAB-FACTS). The task consists of two subtasks: (A) Given a table and a
statement, predicting whether the table supports the statement and (B)
Predicting which cells in the table provide evidence for/against the statement.
We fine-tune TAPAS (a model which extends BERT's architecture to capture
tabular structure) for both the subtasks as it has shown state-of-the-art
performance in various table understanding tasks. In subtask A, we evaluate how
transfer learning and standardizing tables to have a single header row improves
TAPAS' performance. In subtask B, we evaluate how different fine-tuning
strategies can improve TAPAS' performance. Our systems achieve an F1 score of
67.34 in subtask A three-way classification, 72.89 in subtask A two-way
classification, and 62.95 in subtask B.

本文基于 TAPAS 模型扩展 BERT 框架以理解表格语义结构，在 SemEval-2021 任务 9 中解决表格事实的声明验证和证据查找问题，并通过在表头行上进行规范化来优化 TAPAS 在子任务 A 中的表现，在子任务 B 中引入不同的 fine-tuning 策略来提高模型准确率，模型在三种分类任务下分别取得了 67.34、72.89 和 62.95 的 F1 分数。

利用 TAPAS 和迁移学习进行带表格语句验证和证据发现的 SemEval-2021 第 9 任务 Volta

Volta at SemEval-2021 Task 9: Statement Verification and Evidence  Finding with Tables using TAPAS and Transfer Learning

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。