Document-level Neural Machine Translation with Inter-Sentence Attention
Table 2: BLEU scores on the different datasets. The marks “†” after scores indicate that the proposed methods were significantly better than the baseline Transformer at significance level p-value<0.05 Collins et al. (2005). The scores in bold indicate the best ones on the same dataset.
['[BOLD] Models', '[BOLD] TED Talks Zh-En', '[BOLD] TED Talks Es-En', '[BOLD] Subtitles Es-En', '[BOLD] News Es-En']
The baseline is a re-implemented attention-based NMT system RNNSearch* Hinton et al. especially, our model is significantly better than the baseline Transformer at significance level p-value<0.05. Our proposed model outperforms the RNNSearch * baseline by 2.56 BLEU point on the TED Talks (Zh-En) dataset, 2.64 BLEU point on the TED Talks (Es-En) dataset, 0.80 BLEU point on the OpenSubtitles (Es-En) dataset and 1.44 BLEU point on the WMT dataset (Es-En).