What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation (2023.findings-acl)
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| Challenge: | kNN-MT builds an external datastore, which saves all target language token occurrences in the parallel corpus. |
| Approach: | They propose a new paradigm for domain adaptation by building an external datastore which usually saves all target language token occurrences in the parallel corpus. |
| Outcome: | The proposed model can be easily pruned according to local correctness, and it is more explainable. |
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Abudurexiti Reheman, Hongyu Liu, Junhao Ruan, Abudukeyumu Abudula, Yingfeng Luo, Tong Xiao, JingBo Zhu
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| Challenge: | k-Nearest-Neighbor Machine Translation (kNN-MT) is a non-parametric solution for domain adaptation . previous studies have shown that kNN retrieval is at the expense of high latency . |
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| Challenge: | kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
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