Summary of Predicting Anchored Text From Translation Memories For Machine Translation Using Deep Learning Methods, by Richard Yue et al.
Predicting Anchored Text from Translation Memories for Machine Translation Using Deep Learning Methods
by Richard Yue, John E. Ortega
First submitted to arxiv on: 26 Sep 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 The proposed research explores the use of machine learning approaches in fuzzy-match repair (FMR) techniques for computer-aided translation (CAT) tools. Specifically, it investigates the application of Word2Vec, BERT, and ChatGPT in repairing anchored words that follow the continuous bag-of-words (CBOW) paradigm. The authors demonstrate that these models can achieve similar or better results than neural machine translation for translating anchored words from French to English. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses machine learning techniques like Word2Vec, BERT, and ChatGPT to improve fuzzy-match repair in computer-aided translation tools. It shows that these models can help translate words that follow the continuous bag-of-words (CBOW) paradigm. This could make it easier for translators to get accurate translations. |
Keywords
» Artificial intelligence » Bag of words » Bert » Machine learning » Translation » Word2vec
