# Improving Multilingual Neural Machine Translation System for Indic Languages

arXiv: https://arxiv.org/abs/2209.13279 · HTML: https://mlsanity.com/paper/2209.13279/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 78 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/1903.00089/) (2019)
- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism](https://mlsanity.com/paper/1601.01073/) (2016)
- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://mlsanity.com/paper/2105.09501/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [On the Impact of Various Types of Noise on Neural Machine Translation](https://mlsanity.com/paper/1805.12282/) (2018)
- [The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English](https://mlsanity.com/paper/1902.01382/) (2019)
- [The Missing Ingredient in Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/1903.07091/) (2019)
- [Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation](https://mlsanity.com/paper/1708.09803/) (2017)
- [Fully Character-Level Neural Machine Translation without Explicit Segmentation](https://mlsanity.com/paper/1610.03017/) (2016)
- [Learning Language Specific Sub-network for Multilingual Machine Translation](https://mlsanity.com/paper/2105.09259/) (2021)
- [Multi-task Learning for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2010.02523/) (2020)
- [PMIndia -- A Collection of Parallel Corpora of Languages of India](https://mlsanity.com/paper/2001.09907/) (2020)

## Built on by (most cited first, 6 of 6)

- [User-Aware Multilingual Abusive Content Detection in Social Media](https://mlsanity.com/paper/2410.21321/) (2024)
- [A Tulu Resource for Machine Translation](https://mlsanity.com/paper/2403.19142/) (2024)
- [Parallel Corpora for Machine Translation in Low-resource Indic Languages: A Comprehensive Review](https://mlsanity.com/paper/2503.04797/) (2025)
- [Improving the quality of Web-mined Parallel Corpora of Low-Resource Languages using Debiasing Heuristics](https://mlsanity.com/paper/2502.19074/) (2025)
- [Statistical Machine Translation for Indic Languages](https://mlsanity.com/paper/2301.00539/) (2023)
- [An approach for mistranslation removal from popular dataset for Indic MT Task](https://mlsanity.com/paper/2401.06398/) (2024)

## Abstract (arXiv)

Machine Translation System (MTS) serves as an effective tool for communication by translating text or speech from one language to another language. The need of an efficient translation system becomes obvious in a large multilingual environment like India, where English and a set of Indian Languages (ILs) are officially used. In contrast with English, ILs are still entreated as low-resource languages due to unavailability of corpora. In order to address such asymmetric nature, multilingual neural machine translation (MNMT) system evolves as an ideal approach in this direction. In this paper, we propose a MNMT system to address the issues related to low-resource language translation. Our model comprises of two MNMT systems i.e. for English-Indic (one-to-many) and the other for Indic-English (many-to-one) with a shared encoder-decoder containing 15 language pairs (30 translation directions). Since most of IL pairs have scanty amount of parallel corpora, not sufficient for training any machine translation model. We explore various augmentation strategies to improve overall translation quality through the proposed model. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models. In addition, the paper addresses the use of language relationships (in terms of dialect, script, etc.), particularly about the role of high-resource languages of the same family in boosting the performance of low-resource languages. Moreover, the experimental results also show the advantage of backtranslation and domain adaptation for ILs to enhance the translation quality of both source and target languages. Using all these key approaches, our proposed model emerges to be more efficient than the baseline model in terms of evaluation metrics i.e BLEU (BiLingual Evaluation Understudy) score for a set of ILs.