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Tackling Natural Language Inference with a logic-based method is becomingless and less common. While this might have been counterintuitive severaldecades ago, nowadays it seems pretty obvious. The main reasons for such aconception are that (a) logic-based methods are usually brittle when it comesto processing wide-coverage texts, and (b) instead of automatically learningfrom data, they require much of manual effort for development. We make a steptowards to overcome such shortcomings by modeling learning from data asabduction: reversing a theorem-proving procedure to abduce semantic relationsthat serve as the best explanation for the gold label of an inference problem.In other words, instead of proving sentence-level inference relations with thehelp of lexical relations, the lexical relations are proved taking into accountthe sentence-level inference relations. We implement the learning method in atableau theorem prover for natural language and show that it improves theperformance of the theorem prover on the SICK dataset by 1.4% while stillmaintaining high precision (>94%). The obtained results are competitive withthe state of the art among logic-based systems.

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