通过对比表格与汇总摘要，提升表格理解能力
ACCIO: Table Understanding Enhanced via Contrastive Learning with Aggregations
- 用对比学习让原始表格与汇总摘要的表示更接近
- 在列类型标注任务上达到91.1的宏F1分数
- 适合需要精准表格语义理解的研究者
近年来，利用自然语言模型进行表格理解的关注度不断提升。然而，大多数方法仍聚焦于直接学习表格结构。正如人类通过比较句子来增强理解，也能通过比较表格来提升认知。本文提出ACCIO——一种通过对比学习将原始表格与其汇总摘要配对来增强表格理解的新方法。ACCIO训练编码器使这些表格对的表示更接近。通过列类型标注任务验证，其性能达到91.1的宏F1分数，媲美当前最优方法。这是首次尝试使用表格对进行表格嵌入，为表格理解带来显著进展。代码已公开于https://github.com/whnhch/ACCIO/。
原文摘要 · Abstract (English)
The attention to table understanding using recent natural language models has been growing. However, most related works tend to focus on learning the structure of the table directly. Just as humans improve their understanding of sentences by comparing them, they can also enhance their understanding by comparing tables. With this idea, in this paper, we introduce ACCIO, tAble understanding enhanCed via Contrastive learnIng with aggregatiOns, a novel approach to enhancing table understanding by contrasting original tables with their pivot summaries through contrastive learning. ACCIO trains an encoder to bring these table pairs closer together. Through validation via column type annotation, ACCIO achieves competitive performance with a macro F1 score of 91.1 compared to state-of-the-art methods. This work represents the first attempt to utilize pairs of tables for table embedding, promising significant advancements in table comprehension. Our code is available at https://github.com/whnhch/ACCIO/.
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