Non-autoregressive translation (NAT) models generate multiple tokens in one
forward pass and is highly efficient at inference stage compared with
autoregressive translation (AT) methods. However, NAT models often suffer from
the multimodality problem, i.e., generating duplicated tokens or missing
tokens. In this paper, we propose two novel methods to address this issue, the
Look-Around (LA) strategy and the Vocabulary Attention (VA) mechanism. The
Look-Around strategy predicts the neighbor tokens in order to predict the
current token, and the Vocabulary Attention models long-term token dependencies
inside the decoder by attending the whole vocabulary for each position to
acquire knowledge of which token is about to generate. %We also propose a
dynamic bidirectional decoding approach to accelerate the inference process of
the LAVA model while preserving the high-quality of the generated output. Our
proposed model uses significantly less time during inference compared with
autoregressive models and most other NAT models. Our experiments on four
benchmarks (WMT14 En$\rightarrow$De, WMT14 De$\rightarrow$En, WMT16
Ro$\rightarrow$En and IWSLT14 De$\rightarrow$En) show that the proposed model
achieves competitive performance compared with the state-of-the-art
non-autoregressive and autoregressive models while significantly reducing the
time cost in inference phase.

本文提出了两种新方法，即 Look-Around 策略和 Vocabulary Attention 机制，以解决非自回归翻译（NAT）模型中常见的多模态问题，并使用动态双向解码方法加速 LAVA 模型的推理过程，从而显著减少了推理时间成本，同时保持了高质量的生成输出与竞争性能的表现。