@inproceedings{yuan-etal-2024-drama,
    title = "{DRAMA}: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering",
    author = "Yuan, Ruize  and
      Ao, Xiang  and
      Zeng, Li  and
      He, Qing",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.lrec-main.477/",
    pages = "5365--5375",
    abstract = "The TableTextQA task requires finding the answer to the question from a combination of tabular and textual data, which has been gaining increasing attention. The row-based approaches have demonstrated remarkable effectiveness. However, they suffer from the following limitations: (1) a lack of interaction between rows; (2) excessively long input lengths; and (3) question attention shifts in the multi-hop QA task. To this end, we propose a novel method: Dynamic Multi-Granularity Graph Estimate Retrieval - DRAMA. Our method incorporates an interaction mechanism among multiple rows. Specifically, we utilize a memory bank to store the features of each row, thereby facilitating the construction of a heterogeneous graph with multi-row information. Besides, a Dynamic Graph Attention Network (DGAT) module is engaged to gauge the attention shift in the multi-hop question and eliminate the noise information dynamically. Empirical results on the widely used HybridQA and TabFact datasets demonstrate that the proposed model is effective."
}