TINE: A Metric to Assess MT Adequacy

Abstract

We describe TINE, a new automatic evaluation metric for Machine Translation that aims at assessing segment-level adequacy. Lexical similarity and shallow-semantics are used as indicators of adequacy between machine and reference translations. The metric is based on the combination of a lexical matching component and an adequacy component. Lexical matching is performed comparing bagsof-words without any linguistic annotation. The adequacy component consists in: i) using ontologies to align predicates (verbs), ii) using semantic roles to align predicate arguments (core arguments and modifiers), and iii) matching predicate arguments using distributional semantics. TINE’s performance is comparable to that of previous metrics at segment level for several language pairs, with average Kendall’s tau correlation from 0.26 to 0.29. We show that the addition of the shallow-semantic component improves the performance of simple lexical matching strategies and metrics such as BLEU. 1