Title | ||
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Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference. |
Abstract | ||
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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 (\u003e94%). The obtained results are competitive with the state of the art among logic-based systems. |
Year | Venue | DocType |
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2020 | *SEM@COLING | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 |
Name | Order | Citations | PageRank |
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Lasha Abzianidze | 1 | 23 | 6.51 |