Toward Making the Most of Context in Neural Machine Translation
Table 1: Experiment results of our model in comparison with several baselines, including increments of the number of parameters over Transformer baseline (\Delta|{\bm{\theta}}|), training/testing speeds (v_{\rm{train}}/v_{\rm{test}}, some of them are derived from maruf2019selective [maruf2019selective]), and translation results of the testsets in BLEU score.
['[BOLD] Model', '\\Delta|{\\bm{\\theta}}|', 'v_{\\rm{train}}', 'v_{\\rm{test}}', 'Zh-En TED', 'En-De TED', 'En-De News', 'En-De Europarl', 'En-De avg.']
[maruf2019selective, SAN] and Query-guided Capsule Network [yang2019enhancing, QCN]. Among them, our model archives new state-of-the-art results on TED Zh-En and Europarl, showing the superiority of exploiting the whole document context. Though our model is not the best on TED En-De and News tasks, it is still comparable with QCN and HAN and achieves the best average performance on English-German benchmarks by at least 0.47 BLEU score over the best previous model. We suggest this could probably because we did not apply the two-stage training scheme used in \citeauthormiculicich2018document [\citeyearmiculicich2018document] or regularizations introduced in \citeauthoryang2019enhancing [\citeyearyang2019enhancing]. In addition, while sacrificing training speed, the parameter increment and decoding speed could be manageable.