Word Confidence Estimation and its Integration in Sentence Quality Estimation for Machine Translation

Abstract : This paper proposes some ideas to build an effective estima-tor, which predicts the quality of words in a Machine Translation (MT) output. We integrate a number of features of various types (system-based, lexical, syntactic and semantic) into the conventional feature set, for our baseline classifier training. Once having experiments with all features , we deploy a " Feature Selection " strategy to filter the best performing ones. Then, a method that combines multiple " weak " classifiers to build a strong " composite " classifier by taking advantage of their com-plementarity allows us achieve a better performance in term of F score. Finally, we exploit word confidence scores for improving the estimation system at sentence level.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [17 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00953774
Contributor : Laurent Besacier <>
Submitted on : Thursday, November 23, 2017 - 10:10:28 AM
Last modification on : Tuesday, February 12, 2019 - 1:31:30 AM

File

WordConfidenceEstimationAndIts...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00953774, version 1

Collections

Citation

Ngoc-Quang Luong, Laurent Besacier, Benjamin Lecouteux. Word Confidence Estimation and its Integration in Sentence Quality Estimation for Machine Translation. Proceedings of the fifth international conference on knowledge and systems engineering (KSE), 2013, Hanoi, Vietnam. pp.x-x. ⟨hal-00953774⟩

Share

Metrics

Record views

362

Files downloads

95