# Bridging Neural Machine Translation and Bilingual Dictionaries

arXiv: https://arxiv.org/abs/1610.07272 · HTML: https://mlsanity.com/paper/1610.07272/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2016.

## Built on by (most cited first, 18 of 18)

- [Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation](https://mlsanity.com/paper/2302.07856/) (2023)
- [TIM: Teaching Large Language Models to Translate with Comparison](https://mlsanity.com/paper/2307.04408/) (2023)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Chain-of-Dictionary Prompting Elicits Translation in Large Language Models](https://mlsanity.com/paper/2305.06575/) (2023)
- [Continuous Learning in Neural Machine Translation using Bilingual Dictionaries](https://mlsanity.com/paper/2102.06558/) (2021)
- [Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models](https://mlsanity.com/paper/2411.01141/) (2024)
- [Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction](https://mlsanity.com/paper/2105.05498/) (2021)
- [Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation](https://mlsanity.com/paper/2103.07040/) (2021)
- [Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet](https://mlsanity.com/paper/2202.10287/) (2022)
- [Extract and Attend: Improving Entity Translation in Neural Machine Translation](https://mlsanity.com/paper/2306.02242/) (2023)
- [A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages](https://mlsanity.com/paper/2402.01939/) (2024)
- [Improving Rare Word Translation With Dictionaries and Attention Masking](https://mlsanity.com/paper/2408.09075/) (2024)
- [Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach](https://mlsanity.com/paper/2411.08348/) (2024)
- [Cross-lingual Contextualized Phrase Retrieval](https://mlsanity.com/paper/2403.16820/) (2024)
- [SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Models](https://mlsanity.com/paper/2507.18902/) (2025)
- [MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework](https://mlsanity.com/paper/2509.00934/) (2025)
- [LLMs for Low-Resource Dialect Translation Using Context-Aware Prompting: A Case Study on Sylheti](https://mlsanity.com/paper/2511.21761/) (2025)

## Abstract (arXiv)

Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/character model and the other attempts at synthesizing parallel sentences guaranteeing massive occurrence of the translation lexicon. Extensive experiments demonstrate that the proposed methods can remarkably improve the translation quality, and most of the rare words in the test sentences can obtain correct translations if they are covered by the dictionary.