# Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages

arXiv: https://arxiv.org/abs/2206.04439 · HTML: https://mlsanity.com/paper/2206.04439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Neural versus Phrase-Based Machine Translation Quality: a Case Study](https://mlsanity.com/paper/1608.04631/) (2016)
- [Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences](https://mlsanity.com/paper/2007.02671/) (2020)
- [Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation](https://mlsanity.com/paper/1906.05683/) (2019)
- [Neural machine translation for low-resource languages](https://mlsanity.com/paper/1708.05729/) (2017)
- [Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation](https://mlsanity.com/paper/2004.02071/) (2020)
- [Data Diversification: A Simple Strategy For Neural Machine Translation](https://mlsanity.com/paper/1911.01986/) (2019)

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

- [Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation](https://mlsanity.com/paper/2303.15265/) (2023)

## Abstract (arXiv)

Neural Machine Translation (NMT) models have been effective on large bilingual datasets. However, the existing methods and techniques show that the model's performance is highly dependent on the number of examples in training data. For many languages, having such an amount of corpora is a far-fetched dream. Taking inspiration from monolingual speakers exploring new languages using bilingual dictionaries, we investigate the applicability of bilingual dictionaries for languages with extremely low, or no bilingual corpus. In this paper, we explore methods using bilingual dictionaries with an NMT model to improve translations for extremely low resource languages. We extend this work to multilingual systems, exhibiting zero-shot properties. We present a detailed analysis of the effects of the quality of dictionaries, training dataset size, language family, etc., on the translation quality. Results on multiple low-resource test languages show a clear advantage of our bilingual dictionary-based method over the baselines.