| Challenge: | kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants . |
| Approach: | They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks . |
| Outcome: | The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. |
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| Challenge: | Semi-parametric models augment generation with retrieval, but require expensive retrieval operation for every generated token. |
| Approach: | They propose a semi-parametric model which augments generation with retrieval by retrieving tokens from a datastore. |
| Outcome: | The proposed model can retrieve chunks of tokens from the datastore, instead of a single token, with a low decoding speed. |
INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation (2023.acl-long)
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| Challenge: | Neural machine translation models induce a non-smooth representation space, which harms its generalization results. |
| Approach: | They propose a framework to smooth the representation space by adjusting neighbor representations with a small number of new parameters. |
| Outcome: | The proposed framework outperforms the state-of-the-art kNN-MT system with average gains of 1.99 COMET and 1.0 BLEU on four benchmark datasets. |
Adaptive Nearest Neighbor Machine Translation (2021.acl-short)
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| Challenge: | kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Approach: | They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
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Fast Nearest Neighbor Machine Translation (2022.findings-acl)
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| Challenge: | Fast kNN-MT uses the entire corpus as the datastore for the nearest neighbor search . knn-MT is two-orders slower than vanilla MT models . |
| Approach: | They propose a fast kNN-MT model that uses the entire corpus as the datastore for nearest neighbor search. |
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Efficient Cluster-Based k-Nearest-Neighbor Machine Translation (2022.acl-long)
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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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Revisiting Source Context in Nearest Neighbor Machine Translation (2023.emnlp-main)
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| Challenge: | Existing research does not explicitly consider the source context when retrieving similar examples . |
| Approach: | They propose a method to improve neural machine translation via source context enhancement by integrating a source-aware distance calibration module. |
| Outcome: | The proposed approach can be integrated with representative kNN-MT baselines and achieve significant performance improvements. |
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer (2023.emnlp-main)
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| Challenge: | Nearest Neighbor Machine Translation (kNN-MT) is a powerful domain adaptation tool . the reasons for its success have not been thoroughly investigated . |
| Approach: | They propose to integrate pre-trained Neural Machine Translation models with token-level retrieval . they propose to implicitly execute gradient descent on the output projection layer of NMT . |
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Subset Retrieval Nearest Neighbor Machine Translation (2023.acl-long)
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| Challenge: | k-nearest-neighbor machine translation (kNN-MT) is a new approach to improve NMT performance without additional training. |
| Approach: | They propose a method that integrates example-search into the decoding algorithm to improve neighbor token retrieval. |
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Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)
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Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su
| Challenge: | Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains. |
| Approach: | They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore. |
| Outcome: | The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore. |
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | kNN-MT is a non-parametric method that uses nearest neighbor retrieval to translate out-of-domain sentences, rare words, etc. |
| Approach: | They propose a framework that directly uses in-domain monolingual sentences to build an effective datastore for k-nearest-neighbor retrieval. |
| Outcome: | The proposed framework improves translation accuracy with target-side monolingual data while achieving comparable performance with back-translation. |