Domain Machine Translation Method with Dynamic Incorporation of k-Nearest Neighbor Knowledge
HUANG Yuxin1,2, SHEN Tao1,2, JIANG Shuting1,2, ZENG Hao1,2, LAI Hua1,2
1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504; 2. Key Laboratory of Artificial Intelligence in Yunnan Province, Kunming University of Science and Technology, Kunming 650504
Abstract：Domain machine translation methods based on k-nearest neighbour retrieval improve translation quality by incorporating translation knowledge retrieved from a translation knowledge base. Existing methods enhance translation performance by fusing the decoder prediction distribution with k-nearest neighbour knowledge. However, the inaccuracy of the retrieved k-nearest neighbor knowledge may interfere with the prediction results of the model. To address this issue, a domain machine translation method with dynamic incorporation of k-nearest neighbor knowledge is proposed. The confidence of the decoder output distribution is first assessed. With the combination of gating mechanism, the proposed method dynamically decides whether to incorporate the k-nearest-neighbor retrieval results, thereby adjusting the degree of incorporation of k-nearest neighbor knowledge flexibly. The adaptive k-value module is introduced to reduce the interference caused by incorrect k-nearest neighbor knowledge. Besides, the distribution-guided loss is designed to steer the model output approach the target distribution gradually. On four domain-specific German-English machine translation datasets, the proposed method achieves improvements.
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2024, Vol. 37 
