Evaluating Machine Translation Models for English-Hindi Language Pairs: A Comparative Analysis
This work addresses the problem of assessing translation quality for English-Hindi, which is important for users in multilingual contexts, but it is incremental as it focuses on comparative analysis without introducing new methods.
The paper evaluated machine translation models for English-Hindi language pairs using a dataset of over 18,000 parallel sentences and a custom FAQ dataset, finding varying performance levels across different metrics.
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.