Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917928328232960 |
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| author | Lee, Wonjin Kim, Kyumin Lee, Sungjae Lee, Jihun Kim, Kwang In |
| author_facet | Lee, Wonjin Kim, Kyumin Lee, Sungjae Lee, Jihun Kim, Kwang In |
| contents | Applying language models (LMs) to tables is challenging due to the inherent structural differences between two-dimensional tables and one-dimensional text for which the LMs were originally designed. Furthermore, when applying linearized tables to LMs, the maximum token lengths often imposed in self-attention calculations make it difficult to comprehensively understand the context spread across large tables. To address these challenges, we present PieTa (Piece of Table), a new framework for subtable-based question answering (QA). PieTa operates through an iterative process of dividing tables into smaller windows, using LMs to select relevant cells within each window, and merging these cells into a subtable. This multi-resolution approach captures dependencies across multiple rows and columns while avoiding the limitations caused by long context inputs. Instantiated as a simple iterative subtable union algorithm, PieTa demonstrates improved performance over previous subtable-based QA approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07629 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering Lee, Wonjin Kim, Kyumin Lee, Sungjae Lee, Jihun Kim, Kwang In Computation and Language Artificial Intelligence Applying language models (LMs) to tables is challenging due to the inherent structural differences between two-dimensional tables and one-dimensional text for which the LMs were originally designed. Furthermore, when applying linearized tables to LMs, the maximum token lengths often imposed in self-attention calculations make it difficult to comprehensively understand the context spread across large tables. To address these challenges, we present PieTa (Piece of Table), a new framework for subtable-based question answering (QA). PieTa operates through an iterative process of dividing tables into smaller windows, using LMs to select relevant cells within each window, and merging these cells into a subtable. This multi-resolution approach captures dependencies across multiple rows and columns while avoiding the limitations caused by long context inputs. Instantiated as a simple iterative subtable union algorithm, PieTa demonstrates improved performance over previous subtable-based QA approaches. |
| title | Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.07629 |