Paper: Adaptive HTER Estimation for Document-Specific MT Post-Editing

ACL ID P14-1081
Title Adaptive HTER Estimation for Document-Specific MT Post-Editing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

We present an adaptive translation qual- ity estimation (QE) method to predict the human-targeted translation error rate (HTER) for a document-specific machine translation model. We first introduce fea- tures derived internal to the translation de- coding process as well as externally from the source sentence analysis. We show the effectiveness of such features in both classification and regression of MT qual- ity. By dynamically training the QE model for the document-specific MT model, we are able to achieve consistency and pre- diction quality across multiple documents, demonstrated by the higher correlation co- efficient and F-scores in finding Good sen- tences. Additionally, the proposed method is applied to IBM English-to-Japanese MT post editing field study and we observe strong corr...