Paper Info
Title
Character-level machine translation evaluation for languages with ambiguous word boundaries
Abstract
In this work, we introduce the TESLA-CELAB metric (Translation Evaluation of Sentences with Linear-programming-based Analysis -- Character-level Evaluation for Languages with Ambiguous word Boundaries) for automatic machine translation evaluation. For languages such as Chinese where words usually have meaningful internal structure and word boundaries are often fuzzy, TESLA-CELAB acknowledges the advantage of character-level evaluation over word-level evaluation. By reformulating the problem in the linear programming framework, TESLA-CELAB addresses several drawbacks of the character-level metrics, in particular the modeling of synonyms spanning multiple characters. We show empirically that TESLA-CELAB significantly outperforms character-level BLEU in the English-Chinese translation evaluation tasks.
Year
Venue
Keywords
2012
ACL
character-level metrics,english-chinese translation evaluation task,word-level evaluation,linear-programming-based analysis,character-level bleu,character-level machine translation evaluation,word boundary,ambiguous word boundary,translation evaluation,automatic machine translation evaluation,ambiguous word boundaries,character-level evaluation
Field
DocType
Volume
Rule-based machine translation,Example-based machine translation,BLEU,Evaluation of machine translation,Computer science,Machine translation,Fuzzy logic,Machine translation software usability,Natural language processing,Linear programming,Artificial intelligence
Conference
P12-1
Citations 
PageRank 
References 
4
0.38
13
Authors
2
Name
Order
Citations
PageRank
chang liu1876.78
Hwee Tou Ng24092300.40