Machine Translation Evaluation using Recurrent Neural Networks
Rohit Gupta, Constantin Orǎsan, Josef van Genabith · 2015
This paper presents our metric (UoW-LSTM) 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.