# Statistical Machine Translation for Indic Languages

arXiv: https://arxiv.org/abs/2301.00539 · HTML: https://mlsanity.com/paper/2301.00539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 71 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [DGT-TM: A freely Available Translation Memory in 22 Languages](https://mlsanity.com/paper/1309.5226/) (2013)
- [A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions](https://mlsanity.com/paper/1701.02901/) (2017)
- [Neural System Combination for Machine Translation](https://mlsanity.com/paper/1704.06393/) (2017)
- [Improving Multilingual Neural Machine Translation System for Indic Languages](https://mlsanity.com/paper/2209.13279/) (2022)
- [Neural Machine Translation Advised by Statistical Machine Translation](https://mlsanity.com/paper/1610.05150/) (2016)
- [Comparison and Adaptation of Automatic Evaluation Metrics for Quality Assessment of Re-Speaking](https://mlsanity.com/paper/1601.02789/) (2016)
- [SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences](https://mlsanity.com/paper/1812.04898/) (2018)

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

- [An approach for mistranslation removal from popular dataset for Indic MT Task](https://mlsanity.com/paper/2401.06398/) (2024)

## Abstract (arXiv)

Machine Translation (MT) system generally aims at automatic representation of source language into target language retaining the originality of context using various Natural Language Processing (NLP) techniques. Among various NLP methods, Statistical Machine Translation(SMT). SMT uses probabilistic and statistical techniques to analyze information and conversion. This paper canvasses about the development of bilingual SMT models for translating English to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset, all 15 languages are briefed with a short description related to our experimental need. Further, a detailed analysis of Samanantar and OPUS dataset for model building, along with standard benchmark dataset (Flores-200) for fine-tuning and testing, is done as a part of our experiment. Different preprocessing approaches are proposed in this paper to handle the noise of the dataset. To create the system, MOSES open-source SMT toolkit is explored. Distance reordering is utilized with the aim to understand the rules of grammar and context-dependent adjustments through a phrase reordering categorization framework. In our experiment, the quality of the translation is evaluated using standard metrics such as BLEU, METEOR, and RIBES