This paper addresses the search problem in textual inference, where systems need to infer one piece of text from another. A prominent approach to this task is attempts to transform one text into the other through a sequence of inference-preserving transformations, a.k.a. a proof, while estimating the proof's validity. This raises a search challenge of finding the best possible proof. We explore this challenge through a comprehensive investigation of prominent search algorithms and propose two novel algorithmic components specifically designed for textual inference: a gradient-style evaluation function, and a local-lookahead node expansion method. Evaluations, using the open-source system, BiuTee, show the contribution of these ideas to search efficiency and proof quality.
['Asher Stern', 'Roni Stern', 'Ido Dagan', 'Ariel Felner']
Efficient Search for Transformation-based Inference