ACCIO: Table Understanding Enhanced via Contrastive Learning with Aggregations
This work addresses table comprehension for natural language processing applications, representing an incremental advancement by applying contrastive learning to table pairs.
The paper tackles table understanding by introducing ACCIO, which enhances table embeddings through contrastive learning between original tables and their pivot summaries, achieving a macro F1 score of 91.1 in column type annotation.
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/.