  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.
