# Pointer-based Fusion of Bilingual Lexicons into Neural Machine Translation

arXiv: https://arxiv.org/abs/1909.07907 · HTML: https://mlsanity.com/paper/1909.07907/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Lexicon Learning for Few-Shot Neural Sequence Modeling](https://mlsanity.com/paper/2106.03993/) (2021)
- [Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints](https://mlsanity.com/paper/2306.12089/) (2023)
- [Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach](https://mlsanity.com/paper/2409.09009/) (2024)

## Abstract (arXiv)

Neural machine translation (NMT) systems require large amounts of high quality in-domain parallel corpora for training. State-of-the-art NMT systems still face challenges related to out-of-vocabulary words and dealing with low-resource language pairs. In this paper, we propose and compare several models for fusion of bilingual lexicons with an end-to-end trained sequence-to-sequence model for machine translation. The result is a fusion model with two information sources for the decoder: a neural conditional language model and a bilingual lexicon. This fusion model learns how to combine both sources of information in order to produce higher quality translation output. Our experiments show that our proposed models work well in relatively low-resource scenarios, and also effectively reduce the parameter size and training cost for NMT without sacrificing performance.