Relation Alignment for Textual Entailment Recognition

We present an approach to textual entailment recognition, in which inference is based on a shallow semantic representation of relations (predicates and their arguments) in the text and hypothesis of the entailment pair, and in which specialized knowledge is encapsulated in modular components with very simple interfaces. We propose an architecture designed to integrate different, unscaled Natural Language Processing resources, and demonstrate an alignment-based method for combining them. We clarify the purpose of alignment in the RTE task, identifying two distinct alignment models, each of which leads to a different type of entailment system. We identify desirable properties of alignment, and use this to inform our implementation of an alignment component. We evaluate the resulting system on the RTE5 data set, and use an ablation study to assess the conformance of our alignment approach with these desired characteristics.