@inproceedings{c945158875ce4a9080cddc4473de0f5d,
title = "Quality estimation with transformer and RNN architectures",
abstract = "The goal of China Conference on Machine Translation (CCMT 2019) Shared Task on Quality Estimation (QE) is to investigate automatic methods for estimating the quality of Chinese↔English machine translation results without reference translations. This paper presents the submissions of our team for the sentence-level Quality Estimation shared task of CCMT19. Considering the good performance of neural models in previous shared tasks of WMT, our submissions also include two neural-based models: one is Bi-Transformer which proposes the model as a feature extractor with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation, and the other BiRNN architecture uses only two bi-directional RNNs (bi-RNN) with Gated Recurrent Units (GRUs) as encoders, and learns representation of the source and translation sentence pairs to predict the quality of translation outputs.",
keywords = "Quality Estimation, Transformer, Translation evaluation",
author = "Yulin Zhang and Chong Feng and Hongzheng Li",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 15th China Conference on Machine Translation, CCMT 2019 ; Conference date: 27-09-2019 Through 29-09-2019",
year = "2019",
doi = "10.1007/978-981-15-1721-1_7",
language = "English",
isbn = "9789811517204",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "69--76",
editor = "Shujian Huang and Kevin Knight",
booktitle = "Machine Translation - 15th China Conference, CCMT 2019, Revised Selected Papers",
address = "Germany",
}