Papers by Raphaël Mouravieff
Structural Deep Encoding for Table Question Answering (2025.findings-acl)
Copied to clipboard
| Challenge: | Tabular data is a common data format, but many models flatten the structure of a table into a sequence of tokens, resulting in computational costs and over-fitting issues. |
| Approach: | They propose to use special tokens to mark rows and columns, structured embeddings, and sparse attention patterns to preserve structural information of tabular data. |
| Outcome: | The proposed models enhance computational efficiency and preserve structural integrity, leading to better overall performance. |
Learning Relational Decomposition of Queries for Question Answering from Tables (2024.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to Table Question-Answering focus on generating answers directly from inputs, but there are limitations when executing numerical operations. |
| Approach: | They propose to imitate a restricted subset of SQL-like algebraic operations and use them to generate a query. |
| Outcome: | The proposed methods bridge the gap between semantic parsing and direct answering methods and offer valuable insights into which types of operations should be predicted by a generative architecture and which should be executed by an external algorithm. |