Summary of A Data Selection Approach For Enhancing Low Resource Machine Translation Using Cross-lingual Sentence Representations, by Nidhi Kowtal et al.
A Data Selection Approach for Enhancing Low Resource Machine Translation Using Cross-Lingual Sentence Representations
by Nidhi Kowtal, Tejas Deshpande, Raviraj Joshi
First submitted to arxiv on: 4 Sep 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 study tackles the challenge of machine translation in low-resource language pairs, such as English-Marathi, where existing datasets are noisy and impede model performance. The researchers propose a data filtering approach using cross-lingual sentence representations. They leverage a multilingual SBERT model to filter out problematic translations in the training data by assessing semantic equivalence between original and translated sentences. This method improves translation quality over the baseline post-filtering with IndicSBERT, demonstrating its effectiveness in reducing errors in machine translation scenarios with limited resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines translate languages better when there’s not much data to work with. Right now, it’s hard for machines to understand Marathi because the training data is messy. The researchers created a way to clean up this mess by using special computer programs that can compare sentences in different languages. This makes the machine translation more accurate and reliable. |
Keywords
» Artificial intelligence » Translation
