@inproceedings{lin-etal-2023-inner,
    title = "An Inner Table Retriever for Robust Table Question Answering",
    author = "Lin, Weizhe  and
      Blloshmi, Rexhina  and
      Byrne, Bill  and
      de Gispert, Adria  and
      Iglesias, Gonzalo",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.551",
    doi = "10.18653/v1/2023.acl-long.551",
    pages = "9909--9926",
    abstract = "Recent years have witnessed the thriving of pretrained Transformer-based language models for understanding semi-structured tables, with several applications, such as Table Question Answering (TableQA).These models are typically trained on joint tables and surrounding natural language text, by linearizing table content into sequences comprising special tokens and cell information. This yields very long sequences which increase system inefficiency, and moreover, simply truncating long sequences results in information loss for downstream tasks. We propose Inner Table Retriever (ITR), a general-purpose approach for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question. We show that ITR can be easily integrated into existing systems to improve their accuracy with up to 1.3-4.8{\%} and achieve state-of-the-art results in two benchmarks, i.e., 63.4{\%} in WikiTableQuestions and 92.1{\%} in WikiSQL. Additionally, we show that ITR makes TableQA systems more robust to reduced model capacity and to different ordering of columns and rows. We make our code available at: \url{https://github.com/amazon-science/robust-tableqa}.",
}

Recent years have witnessed the thriving of pretrained Transformer-based language models for understanding semi-structured tables, with several applications, such as Table Question Answering (TableQA).These models are typically trained on joint tables and surrounding natural language text, by linearizing table content into sequences comprising special tokens and cell information. This yields very long sequences which increase system inefficiency, and moreover, simply truncating long sequences results in information loss for downstream tasks. We propose Inner Table Retriever (ITR), a general-purpose approach for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question. We show that ITR can be easily integrated into existing systems to improve their accuracy with up to 1.3-4.8{\%} and achieve state-of-the-art results in two benchmarks, i.e., 63.4{\%} in WikiTableQuestions and 92.1{\%} in WikiSQL. Additionally, we show that ITR makes TableQA systems more robust to reduced model capacity and to different ordering of columns and rows. We make our code available at: \url{https://github.com/amazon-science/robust-tableqa}.
Lin, Weizhe Blloshmi, Rexhina Byrne, Bill de Gispert, Adria Iglesias, Gonzalo
An Inner Table Retriever for Robust Table Question Answering