Asynchronous and Segmented Bidirectional Encoding for NMT
Jingpu Yang Zehua Han Mengyu Xiang Helin Wang Yuxiao Huang Miao Fang
With the rapid advancement of Neural Machine Translation (NMT), enhancing
translation efficiency and quality has become a focal point of research.
Despite the commendable performance of general models such as the Transformer
in various aspects, they still fall short in processing long sentences and
fully leveraging bidirectional contextual information. This paper introduces an
improved model based on the Transformer, implementing an asynchronous and
segmented bidirectional decoding strategy aimed at elevating translation
efficiency and accuracy. Compared to traditional unidirectional translations
from left-to-right or right-to-left, our method demonstrates heightened
efficiency and improved translation quality, particularly in handling long
sentences. Experimental results on the IWSLT2017 dataset confirm the
effectiveness of our approach in accelerating translation and increasing
accuracy, especially surpassing traditional unidirectional strategies in long
sentence translation. Furthermore, this study analyzes the impact of sentence
length on decoding outcomes and explores the model's performance in various
scenarios. The findings of this research not only provide an effective encoding
strategy for the NMT field but also pave new avenues and directions for future
studies.