@inproceedings{yuan-etal-2024-drama-dynamic,
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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering
%A Yuan, Ruize
%A Ao, Xiang
%A Zeng, Li
%A He, Qing
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F yuan-etal-2024-drama-dynamic
%X 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.
%U https://aclanthology.org/2024.lrec-main.477
%P 5365-5375
Markdown (Informal)
[DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering](https://aclanthology.org/2024.lrec-main.477) (Yuan et al., LREC-COLING 2024)
ACL