Lexical cohesion for evaluation of machine translation at document level

Billy T.M. Wong, Cecilia F.K. Pun, Chunyu Kit, Jonathan J. Webster

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

This paper studies how granularity of machine translation evaluation can be extended from sentence to document level. While most state-of-the-art evaluation metrics focus on the sentence level, we emphasize the importance of document structure, showing that lexical cohesion is a critical feature to highlight the superior quality of human translation to machine translation, which uses cohesive devices to tie salient words between sentences together as a text. An experiment shows that this feature can bring forth a 3-5% improvement in the correlation of automatic evaluation results with human judgments of machine translation outputs at the document level.

Original languageEnglish
Title of host publicationNLP-KE 2011 - Proceedings of the 7th International Conference on Natural Language Processing and Knowledge Engineering
Pages238-242
Number of pages5
DOIs
Publication statusPublished - 2011
Event7th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2011 - Tokushima, Japan
Duration: 27 Nov 201129 Nov 2011

Publication series

NameNLP-KE 2011 - Proceedings of the 7th International Conference on Natural Language Processing and Knowledge Engineering

Conference

Conference7th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2011
Country/TerritoryJapan
CityTokushima
Period27/11/1129/11/11

Keywords

  • evaluation metric
  • lexical cohesion
  • machine translation evaluation
  • text coherence

Fingerprint

Dive into the research topics of 'Lexical cohesion for evaluation of machine translation at document level'. Together they form a unique fingerprint.

Cite this