Lexical Access Preference and Constraint Strategies for Improving Multiword Expression Association within Semantic MT Evaluation

Abstract

We examine lexical access preferences and constraints in computing multiword expression associations from the standpoint of a high-impact extrinsic task-based performance measure, namely semantic machine translation evaluation. In automated MT evaluation metrics, machine translations are compared against human reference translations, which are almost never worded exactly the same way except in the most trivial of cases. Because of this, one of the most important factors in correctly predicting semantic translation adequacy is the accuracy of recognizing alternative lexical realizations of the same multiword expressions in semantic role fillers. Our results comparing bag-of-words, maximum alignment, and inversion transduction grammars indicate that cognitively motivated ITGs provide superior lexical access characteristics for multiword expression associations, leading to state-of-the-art improvements in correlation with human adequacy judgments.