Lightly-Supervised Word Sense Translation Error Detection for an Interactive Conversational Spoken Language Translation System

Abstract

Lexical ambiguity can lead to concept transfer failure in conversational spoken language translation (CSLT) systems. This paper presents a novel, classification-based approach to accurately detecting word sense translation errors (WSTEs) of ambiguous source words. The approach requires minimal human annotation effort, and can be easily scaled to new language pairs and domains, with only a word-aligned parallel corpus and a small set of manual translation judgments. We show that this approach is highly precise in detecting WSTEs, even in highly skewed data, making it practical for use in an interactive CSLT system.