Summary of Language Modelling Approaches to Adaptive Machine Translation, by Yasmin Moslem
Language Modelling Approaches to Adaptive Machine Translation
by Yasmin Moslem
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
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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 Machine learning educators will appreciate this paper’s contribution to machine translation (MT). Researchers have made significant progress in adapting MT to specific domains. However, a common challenge is the scarcity of in-domain data, which can lead to inconsistent translations. Large language models (LLMs) have shown promise in replicating input-output text generation patterns without fine-tuning. This work explores whether LLMs can improve adaptive MT quality during inference time and enhance domain adaptation when sufficient in-domain data is lacking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine translation better. When we translate words from one language to another, it’s important that the result makes sense in the new language. Right now, machines are good at translating general texts but struggle with specific topics like medicine or law. The problem is that there isn’t enough training data for these areas. This paper looks into using special computer models called large language models to make translations more accurate and consistent. |
Keywords
» Artificial intelligence » Domain adaptation » Fine tuning » Inference » Machine learning » Text generation » Translation
