Non-autoregressive neural machine translation (NAT) generates each target word in parallel and has achieved promising inference acceleration. However, existing NAT models still have a big gap in translation quality compared to autoregressive neural machine translation models due to the multimodality problem: the target words may come from multiple feasible translations. To address this problem, we propose a novel NAT framework ReorderNAT which explicitly models the reordering information to guide the decoding of NAT. Specially, ReorderNAT utilizes deterministic and non-deterministic decoding strategies that leverage reordering information as a proxy for the final translation to encourage the decoder to choose words belonging to the same translation. Experimental results on various widely-used datasets show that our proposed model achieves better performance compared to most existing NAT models, and even achieves comparable translation quality as autoregressive translation models with a significant speedup.

Non-autoregressive neural machine translation (NAT) predicts the entire target sequence simultaneously and significantly accelerates inference process.However, NAT discards the dependency information in a sentence, and thus inevitably suffers from the multi-modality problem: the target tokens may be provided by different possible translations, often causing token repetitions or missing.To alleviate this problem, we propose a novel semiautoregressive model RecoverSAT in this work, which generates a translation as a sequence of segments.The segments are generated simultaneously while each segment is predicted token-by-token.By dynamically determining segment length and deleting repetitive segments, RecoverSAT is capable of recovering from repetitive and missing token errors.Experimental results on three widelyused benchmark datasets show that our proposed model achieves more than 4× speedup while maintaining comparable performance compared with the corresponding autoregressive model. * indicates equal contribution † indicates corresponding author Src.es gibt heute viele Farmer mit diesem AnsatzFeasible there are lots of farmers doing this today Trans.there are a lot of farmers doing this todayTrans. 1 there are lots of of farmers doing this today Trans. 2 there are a lot farmers doing this today

Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, Zhiyuan Liu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.