Capturing document context inside sentence-level neural machine translation models with self-training
Table 1: Results on NIST evaluation sets. The first four rows show the performance of the previous document-level NMT models from (Wang et al., 2017; Kuang et al., 2017; Zhang et al., 2018). The last four rows show performance of our baseline sentence-level Transformer models with and without self-training. BT: backtranslation.
['Method', 'Model', 'MT06', 'MT03', 'MT04', 'MT05', 'MT08']
The self-training procedure improves the results of our sentence-level baseline by the average of 0.53 BLEU for non-backtranslated model and by 0.93 BLEU for backtranslated model for all evaluation sets. Our baseline sentence-level Transformer model trained without backtranslation outperforms previous document-level models by Wang et al. Backtranslation further improves the results of our sentence-level model leading to higher BLEU score compared to the Document Transformer Zhang et al.