Making Implicit Premises Explicit in Logical Understanding of Enthymemes
This addresses the lack of systematic methods for logical understanding of enthymemes in NLP, which is an incremental improvement for applications in argument analysis and reasoning.
The paper tackles the problem of decoding the underlying logic in enthymemes (arguments with implicit premises) by proposing a pipeline that integrates LLMs for generating implicit premises and translating natural language to logical formulas, and a neuro-symbolic reasoner for entailment, achieving promising performance on two datasets as measured by precision, recall, F1-score, and accuracy.
Real-world arguments in text and dialogues are normally enthymemes (i.e. some of their premises and/or claims are implicit). Natural language processing (NLP) methods for handling enthymemes can potentially identify enthymemes in text but they do not decode their underlying logic, whereas logic-based approaches for handling them assume a knowledgebase with sufficient formulae that can be used to decode them via abduction. There is therefore a lack of a systematic method for translating textual components of an enthymeme into a logical argument and generating the logical formulae required for their decoding, and thereby showing logical entailment. To address this, we propose a pipeline that integrates: (1) a large language model (LLM) to generate intermediate implicit premises based on the explicit premise and claim; (2) another LLM to translate the natural language into logical formulas; and (3) a neuro-symbolic reasoner based on a SAT solver to determine entailment. We evaluate our pipeline on two enthymeme datasets, demonstrating promising performance in selecting the correct implicit premise, as measured by precision, recall, F1-score, and accuracy.