This paper proposes the utilization of lexical cohesion to facilitate evaluation of machine translation at the document level. As a linguistic means to achieve text coherence, lexical cohesion ties sentences together into a meaningfully interwoven structure through words with the same or related meaning. A comparison between machine and human translation is conducted to illustrate one of their critical distinctions that human translators tend to use more cohesion devices than machine. Various ways to apply this feature to evaluate machinetranslated documents are presented, including one without reliance on reference translation. Experimental results show that incorporating this feature into sentence-level evaluation metrics can enhance their correlation with human judgements.
Wong and REF incorporate lexical cohesion to machine translation evaluation metrics to evaluate document-level machine translation quality.
Extending Machine Translation Evaluation Metrics with Lexical Cohesion to Document Level
EMNLP
Computer Science