A Semantic Parsing Algorithm to Solve Linear Ordering Problems

• URL: http://arxiv.org/abs/2502.08415v1
• Date: Wed, 12 Feb 2025 13:58:42 GMT
• Title: A Semantic Parsing Algorithm to Solve Linear Ordering Problems
• Authors: Maha Alkhairy, Vincent Homer, Brendan O'Connor,
• Abstract summary: We develop an algorithm to semantically parse linear ordering problems. Our method takes as input a number of premises and candidate statements. We then utilize constraint logic programming to infer the truth of proposed statements about the ordering.
• Score: 2.23890712706409
• License:
• Abstract: We develop an algorithm to semantically parse linear ordering problems, which require a model to arrange entities using deductive reasoning. Our method takes as input a number of premises and candidate statements, parsing them to a first-order logic of an ordering domain, and then utilizes constraint logic programming to infer the truth of proposed statements about the ordering. Our semantic parser transforms Heim and Kratzer's syntax-based compositional formal semantic rules to a computational algorithm. This transformation involves introducing abstract types and templates based on their rules, and introduces a dynamic component to interpret entities within a contextual framework. Our symbolic system, the Formal Semantic Logic Inferer (FSLI), is applied to answer multiple choice questions in BIG-bench's logical_deduction multiple choice problems, achieving perfect accuracy, compared to 67.06% for the best-performing LLM (GPT-4) and 87.63% for the hybrid system Logic-LM. These promising results demonstrate the benefit of developing a semantic parsing algorithm driven by first-order logic constructs.

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