Abstract
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.
Abstract (translated)
随着 Neural Machine Translation（NMT）的快速发展，提高翻译的效率和质量已成为研究的重点。尽管在各种方面表现出优异性能的通用模型（如Transformer）仍然在处理长句子和充分利用双向上下文信息方面存在不足，但该模型在提高翻译效率和质量方面仍然具有很大的潜力。本文介绍了一种基于Transformer的改进模型，通过实现异步和分割的上下文双向解码策略，旨在提高翻译的效率和准确性。与从左到右或从右到左的传统无向翻译相比，我们的方法在处理长句子方面表现出更高的效率和更好的翻译质量。实验结果表明，我们的方法可以加速翻译并提高准确性，特别是在长句子翻译方面超过了传统的无向策略。此外，本研究分析了句子长度对解码结果的影响，并探讨了模型在不同场景下的性能。这一研究为 NMT 领域提供了有效的编码策略，同时也为未来的研究开辟了新的途径和方向。
URL
https://arxiv.org/abs/2402.14849
