Explainable Lexical Entailment with Semantic Graphs.

We present novel methods for detecting lexical entailment in a fully rule-based and explainable fashion, by automatic construction of semantic graphs, in any language for which a crowd-sourced dictionary with sufficient coverage and a dependency parser of sufficient accuracy are available. We experiment and evaluate on both the Semeval-2020 lexical entailment task (Glavas et al. (2020). Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 24-35) and the SherLIiC lexical inference dataset of typed predicates (Schmitt and Schutze (2019). Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 902-914). Combined with top-performing systems, our method achieves improvements over the previous state-of-the-art on both benchmarks. As a standalone system, it offers a fully interpretable model of lexical entailment that makes detailed error analysis possible, uncovering future directions for improving both the semantic parsing method and the inference process on semantic graphs. We release all components of our system as open source software.