Machine Translation Evaluation using Recurrent Neural Networks

Gupta, R., Orasan, C., & van Genabith, J. (2015). Machine Translation Evaluation using Recurrent Neural Networks. In Proceedings of the Tenth Workshop on Statistical Machine Translation (pp. 380384). Lisbon, Portugal.

Abstract

This paper presents our metric (UoWLSTM) submitted in the WMT-15 metrics task. Many state-of-the-art Machine Translation (MT) evaluation metrics are complex, involve extensive external resources (e.g. for paraphrasing) and require tuning to achieve the best results. We use a metric based on dense vector spaces and Long Short Term Memory (LSTM) networks, which are types of Recurrent Neural Networks (RNNs). For WMT- 15 our new metric is the best performing metric overall according to Spearman and Pearson (Pre-TrueSkill) and second best according to Pearson (TrueSkill) system level correlation.

Electronic version

http://www.statmt.org/wmt15/pdf/WMT47.pdf