Contextual Encoding for Translation Quality Estimation

Junjie Hu, Wei-Cheng Chang, Yuexin Wu, Graham Neubig


Abstract
The task of word-level quality estimation (QE) consists of taking a source sentence and machine-generated translation, and predicting which words in the output are correct and which are wrong. In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach. The first part uses an embedding layer to represent words and their part-of-speech tags in both languages. The second part leverages a one-dimensional convolution layer to integrate local context information for each target word. The third part applies a stack of feed-forward and recurrent neural networks to further encode the global context in the sentence before making the predictions. This model was submitted as the CMU entry to the WMT2018 shared task on QE, and achieves strong results, ranking first in three of the six tracks.
Anthology ID:
W18-6462
Volume:
Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:
October
Year:
2018
Address:
Belgium, Brussels
Venues:
EMNLP | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
788–793
Language:
URL:
https://www.aclweb.org/anthology/W18-6462
DOI:
10.18653/v1/W18-6462
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6462.pdf