Evaluating Machine Translation Models for English-Hindi Language Pairs: A Comparative Analysis
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913859467476992 |
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| author | Shetty, Ahan Prasannakumar |
| author_facet | Shetty, Ahan Prasannakumar |
| contents | Machine translation has become a critical tool in bridging linguistic gaps, especially between languages as diverse as English and Hindi. This paper comprehensively evaluates various machine translation models for translating between English and Hindi. We assess the performance of these models using a diverse set of automatic evaluation metrics, both lexical and machine learning-based metrics. Our evaluation leverages an 18000+ corpus of English Hindi parallel dataset and a custom FAQ dataset comprising questions from government websites. The study aims to provide insights into the effectiveness of different machine translation approaches in handling both general and specialized language domains. Results indicate varying performance levels across different metrics, highlighting strengths and areas for improvement in current translation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Machine Translation Models for English-Hindi Language Pairs: A Comparative Analysis Shetty, Ahan Prasannakumar Computation and Language Machine Learning Machine translation has become a critical tool in bridging linguistic gaps, especially between languages as diverse as English and Hindi. This paper comprehensively evaluates various machine translation models for translating between English and Hindi. We assess the performance of these models using a diverse set of automatic evaluation metrics, both lexical and machine learning-based metrics. Our evaluation leverages an 18000+ corpus of English Hindi parallel dataset and a custom FAQ dataset comprising questions from government websites. The study aims to provide insights into the effectiveness of different machine translation approaches in handling both general and specialized language domains. Results indicate varying performance levels across different metrics, highlighting strengths and areas for improvement in current translation systems. |
| title | Evaluating Machine Translation Models for English-Hindi Language Pairs: A Comparative Analysis |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2505.19604 |