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融入上下文特征提取的非自回归神经机器翻译

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非自回归翻译(NAT)模型是一种消除目标句子中的顺序依赖关系的翻译模型,在推理速度上取得了显著的突破.然而该模型在翻译质量方面存在一些局限,为探究原因,主要对注意机制进行了细致而全面的初步研究,研究结果揭示了NAT模型在捕捉局部性特征方面存在明显不足.为此提出了一种通过明确引入周围词汇信息而改进NAT模型局部性能力的方法.具体而言,在编码器和解码器两个方向上引入了混合分组线性变换,以获得更具局部感知性的表示.通过在WMT14 英德与WMT16 英罗两个数据集上进行实验,结果表明该方法以微弱的速度代价分别提高了0.7 与1.03 个BLEU分数,这表明该研究方法在改善NAT模型的局部性特征提取方面具有显著的效果和潜力.
Non-autoregressive neural machine translation with contextual feature integration
The Non-autoregressive Translation(NAT)model is a translation model that has made a sig-nificant breakthrough in inference speed by eliminating order dependencies in target sentences.However,the model has some limitations in translation quality.To investigate the fundamental reasons for this,this paper conducts a detailed and comprehensive preliminary study of attentional mechanisms,and the results reveal that the NAT model is inadequate in capturing localization features.Therefore,in this paper,a method for impro-ving the localization capability of the NAT model by explicitly introducing the surrounding lexical information is proposed.Specifically,Hybrid Grouped Linear Transformations are introduced in both the encoder and decod-er directions to attain representations that are more locally sensitive.Through experiments on the WMT14 Eng-lish-German and WMT16 English-Romanian datasets,the results demonstrate that the method improves the BLEU scores of 0.7 and 1.03 at a minor speed cost,respectively,which suggests that the proposed method has a significant effect and potential to enhance the localizability feature extraction of NAT models.

non-autoregressivelocal characteristicshybrid grouped linear transformationsautore-gressive

赵光耀、王剑、高盛祥、余正涛

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昆明理工大学 信息工程与自动化学院,云南 昆明 650504

云南省人工智能重点实验室,云南 昆明 650504

非自回归 局部性特征 混合分组线性变换 自回归

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金云南省高新技术产业发展项目云南省重点研发计划云南省重点研发计划

623761116236602761972186U21B2027201606202103AA080015202303AP140008

2024

陕西理工大学学报(自然科学版)
陕西理工学院

陕西理工大学学报(自然科学版)

影响因子：0.425
ISSN：2096-3998
年,卷(期)：2024.40(3)
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