Text-to-Table Relationship Extraction (TTRE)3 has emerged as a significant research topic. Although tables enable humans to com- prehend complex data structures quickly, machines often struggle with such interpretations. The primary challenge of this paper lies in understanding the myriad intentions behind the table’s creation and the possible ambiguity when viewed without context. We pr pose an approach to address these issues by embedding a table in a textual context. Specifically, we convert tables contained in HTML- formatted documents to the Markdown format and create training data that combine the tables with information about the associated question text and elements. Then, we use the training data to train a QLoRA model based on llama2-13b-chat-hf. This approach promotes holistic interpretation of tables and their associated texts within a single vector space.
Higa et al. (Tue,) studied this question.
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