# Quality Estimation Of Machine Translation Outputs Through Stemming

arXiv: https://arxiv.org/abs/1407.2694 · HTML: https://mlsanity.com/paper/1407.2694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2014.

## Built on by (most cited first, 1 of 1)

- [T5Score: A Methodology for Automatically Assessing the Quality of LLM Generated Multi-Document Topic Sets](https://mlsanity.com/paper/2407.17390/) (2024)

## Abstract (arXiv)

Machine Translation is the challenging problem for Indian languages. Every day we can see some machine translators being developed, but getting a high quality automatic translation is still a very distant dream . The correct translated sentence for Hindi language is rarely found. In this paper, we are emphasizing on English-Hindi language pair, so in order to preserve the correct MT output we present a ranking system, which employs some machine learning techniques and morphological features. In ranking no human intervention is required. We have also validated our results by comparing it with human ranking.