# Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation

arXiv: https://arxiv.org/abs/2004.02071 · HTML: https://mlsanity.com/paper/2004.02071/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation](https://mlsanity.com/paper/2303.15265/) (2023)
- [Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation](https://mlsanity.com/paper/2205.08722/) (2022)
- [Extract and Attend: Improving Entity Translation in Neural Machine Translation](https://mlsanity.com/paper/2306.02242/) (2023)
- [Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages](https://mlsanity.com/paper/2206.04439/) (2022)
- [From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2412.00966/) (2024)
- [In-Domain African Languages Translation Using LLMs and Multi-armed Bandits](https://mlsanity.com/paper/2505.15069/) (2025)
- [Resource-Lean Lexicon Induction for German Dialects](https://mlsanity.com/paper/2604.23824/) (2026)

## Abstract (arXiv)

We explore ways of incorporating bilingual dictionaries to enable semi-supervised neural machine translation. Conventional back-translation methods have shown success in leveraging target side monolingual data. However, since the quality of back-translation models is tied to the size of the available parallel corpora, this could adversely impact the synthetically generated sentences in a low resource setting. We propose a simple data augmentation technique to address both this shortcoming. We incorporate widely available bilingual dictionaries that yield word-by-word translations to generate synthetic sentences. This automatically expands the vocabulary of the model while maintaining high quality content. Our method shows an appreciable improvement in performance over strong baselines.