@inproceedings{kojima-2024-sub,
    title = "Sub-Table Rescorer for Table Question Answering",
    author = "Kojima, Atsushi",
    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.1339",
    pages = "15422--15427",
    abstract = "We propose a sub-table rescorer (STR) to improve the performance of an inner table retriever (ITR)-based inference for the table question answering. Tabular language model (TLM) truncates the sequence of a long table due to their input token limits. It leads to accuracy degradation. To solve this problem, ITR extracts sub-table candidates, which correspond to a part of an entire greater original table on the basis of relevance scores to the question for each of the columns and rows. Then, the topN longest sub-tables are selected. Our proposed STR estimates the relevance score between a question and each sub-table. In this work, we explored two different methods to integrate STR to the ITR-based inference. In the first method, STR rescores sub-table candidates, and the topN sub-tables are chosen. Then, TLM outputs the most confident answer. In the second method, the score calculated by STR is interpolated with the score calculated by TLM. Then, the most confident answer is chosen. In the experiment, we evaluate the performance on the WikiTableQuestions dataset. By applying STR to the ITR-based inference, we observed 4.4{\%} and 6.3{\%} relative reductions in error rate in the rescoring- and score-fusion-based methods, respectively.",
}

We propose a sub-table rescorer (STR) to improve the performance of an inner table retriever (ITR)-based inference for the table question answering. Tabular language model (TLM) truncates the sequence of a long table due to their input token limits. It leads to accuracy degradation. To solve this problem, ITR extracts sub-table candidates, which correspond to a part of an entire greater original table on the basis of relevance scores to the question for each of the columns and rows. Then, the topN longest sub-tables are selected. Our proposed STR estimates the relevance score between a question and each sub-table. In this work, we explored two different methods to integrate STR to the ITR-based inference. In the first method, STR rescores sub-table candidates, and the topN sub-tables are chosen. Then, TLM outputs the most confident answer. In the second method, the score calculated by STR is interpolated with the score calculated by TLM. Then, the most confident answer is chosen. In the experiment, we evaluate the performance on the WikiTableQuestions dataset. By applying STR to the ITR-based inference, we observed 4.4{\%} and 6.3{\%} relative reductions in error rate in the rescoring- and score-fusion-based methods, respectively.
Kojima, Atsushi
Sub-Table Rescorer for Table Question Answering