Summary of Self-generated Replay Memories For Continual Neural Machine Translation, by Michele Resta and Davide Bacciu
Self-generated Replay Memories for Continual Neural Machine Translation
by Michele Resta, Davide Bacciu
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper proposes a novel approach to continually learning Neural Machine Translation systems, addressing the issue of catastrophic forgetting that hinders continuous improvement. By leveraging the generative ability of encoder-decoder Transformers and using a replay memory populated by the model itself as a generator of parallel sentences, the authors demonstrate effective counteraction of catastrophic forgetting without requiring explicit memorization of training data. The approach is evaluated empirically on Neural Machine Translation systems, showcasing improved performance on a stream of experiences comprising different languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Neural Machine Translation systems learn new languages and improve over time. Right now, these systems are great at translating some languages but struggle to remember what they learned before. To fix this, the researchers use a special kind of computer model that can generate text on its own. They show that by using this model to create fake parallel sentences, they can help the Neural Machine Translation system learn new things without forgetting what it knew before. |
Keywords
* Artificial intelligence * Encoder decoder * Translation
