Logical Inference for Counting on Semi-structured Tables
This addresses a specific bottleneck in NLI for semi-structured tables, offering an incremental improvement for tasks requiring numerical understanding.
The paper tackles the problem of numerical inference, such as counting, in Natural Language Inference (NLI) for semi-structured tables, proposing a logical inference system that uses model checking to achieve more robust performance compared to neural approaches.
Recently, the Natural Language Inference (NLI) task has been studied for semi-structured tables that do not have a strict format. Although neural approaches have achieved high performance in various types of NLI, including NLI between semi-structured tables and texts, they still have difficulty in performing a numerical type of inference, such as counting. To handle a numerical type of inference, we propose a logical inference system for reasoning between semi-structured tables and texts. We use logical representations as meaning representations for tables and texts and use model checking to handle a numerical type of inference between texts and tables. To evaluate the extent to which our system can perform inference with numerical comparatives, we make an evaluation protocol that focuses on numerical understanding between semi-structured tables and texts in English. We show that our system can more robustly perform inference between tables and texts that requires numerical understanding compared with current neural approaches.