Summary of Simply Trainable Nearest Neighbour Machine Translation with Gpu Inference, by Hossam Amer et al.
Simply Trainable Nearest Neighbour Machine Translation with GPU Inference
by Hossam Amer, Abdelrahman Abouelenin, Mohamed Maher, Evram Narouz, Mohamed Afify, Hany Awadallah
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a simple and trainable nearest neighbor machine translation (knnMT) model to improve the efficiency and quality of domain adaptation. The proposed method builds upon existing work by Dai et al., which uses distance-aware interpolation to adapt pre-trained transformers for new domains. This approach constructs a small datastore for each input sentence and trains a single-layer network to interpolate between knnMT and pre-trained results. Experimental results show that the proposed method either improves or maintains translation quality while being automatic, with only a 5% drop in GPU inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new machine learning approach called nearest neighbor machine translation (knnMT) that helps computers quickly adapt to different languages and topics without needing to be retrained. KnnMT works by looking at how words are related in a big database of text, and then using this information to translate new sentences. The researchers who wrote the paper wanted to make knnMT faster and better, so they came up with a simple way to train it on small amounts of new data. They tested their method on different types of texts and found that it worked just as well or even better than other approaches. |
Keywords
» Artificial intelligence » Domain adaptation » Inference » Machine learning » Nearest neighbor » Translation
