Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference

• URL: http://arxiv.org/abs/2010.15909v2
• Date: Tue, 1 Dec 2020 12:02:44 GMT
• Title: Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference
• Authors: Lasha Abzianidze
• Abstract summary: We implement a learning method in a theorem prover for natural language. We show that it improves the performance of the theorem prover on the SICK dataset by 1.4% while still maintaining high precision. The obtained results are competitive with the state of the art among logic-based systems.
• Score: 0.4962199635155534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tackling Natural Language Inference with a logic-based method is becoming less and less common. While this might have been counterintuitive several decades ago, nowadays it seems pretty obvious. The main reasons for such a conception are that (a) logic-based methods are usually brittle when it comes to processing wide-coverage texts, and (b) instead of automatically learning from data, they require much of manual effort for development. We make a step towards to overcome such shortcomings by modeling learning from data as abduction: reversing a theorem-proving procedure to abduce semantic relations that serve as the best explanation for the gold label of an inference problem. In other words, instead of proving sentence-level inference relations with the help of lexical relations, the lexical relations are proved taking into account the sentence-level inference relations. We implement the learning method in a tableau theorem prover for natural language and show that it improves the performance of the theorem prover on the SICK dataset by 1.4% while still maintaining high precision (>94%). The obtained results are competitive with the state of the art among logic-based systems.

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