Summary of From Priest to Doctor: Domain Adaptation For Low-resource Neural Machine Translation, by Ali Marashian et al.
From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation
by Ali Marashian, Enora Rice, Luke Gessler, Alexis Palmer, Katharina von der Wense
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 evaluates various domain adaptation (DA) and neural machine translation (NMT) methods in a realistic setting where only limited parallel data are available. Specifically, it explores the effectiveness of different approaches in translating between a high-resource language and a low-resource language with access to parallel Bible data, bilingual dictionaries, and monolingual target-domain corpora. The results show that the simplest method, DALI, performs well, but there is still room for improvement through further investigation into DA for low-resource NMT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to improve machine translation when we don’t have much data. Right now, many languages don’t have enough data to train good translators. This makes it hard to translate texts from those languages accurately. The researchers in this study try different methods to see which ones work best for translating between a language with plenty of training data and one with very little. They find that the simplest method is actually pretty effective, but there’s still more to learn about how to improve translation when we have limited data. |
Keywords
» Artificial intelligence » Domain adaptation » Translation
