# Evaluating Machine Translation Models for English-Hindi Language Pairs: A Comparative Analysis

arXiv: https://arxiv.org/abs/2505.19604 · HTML: https://mlsanity.com/paper/2505.19604/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 5 of 5)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages](https://mlsanity.com/paper/2305.16307/) (2023)
- [Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation](https://mlsanity.com/paper/1409.1257/) (2014)
- [A Hybrid Approach For Hindi-English Machine Translation](https://mlsanity.com/paper/1702.01587/) (2017)

## Abstract (arXiv)

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.