# Towards one-shot learning for rare-word translation with external experts

arXiv: https://arxiv.org/abs/1809.03182 · HTML: https://mlsanity.com/paper/1809.03182/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 10 of 10)

- [Lexicon Learning for Few-Shot Neural Sequence Modeling](https://mlsanity.com/paper/2106.03993/) (2021)
- [Few-shot learning for medical text: A systematic review](https://mlsanity.com/paper/2204.14081/) (2022)
- [Continuous Learning in Neural Machine Translation using Bilingual Dictionaries](https://mlsanity.com/paper/2102.06558/) (2021)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [Encouraging Neural Machine Translation to Satisfy Terminology Constraints](https://mlsanity.com/paper/2106.03730/) (2021)
- [Compositionality as Lexical Symmetry](https://mlsanity.com/paper/2201.12926/) (2022)
- [MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations](https://mlsanity.com/paper/2310.11634/) (2023)
- [Audience-specific Explanations for Machine Translation](https://mlsanity.com/paper/2309.12998/) (2023)
- [Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach](https://mlsanity.com/paper/2409.09009/) (2024)
- [Knowledge Based Template Machine Translation In Low-Resource Setting](https://mlsanity.com/paper/2209.03554/) (2022)

## Abstract (arXiv)

Neural machine translation (NMT) has significantly improved the quality of automatic translation models. One of the main challenges in current systems is the translation of rare words. We present a generic approach to address this weakness by having external models annotate the training data as Experts, and control the model-expert interaction with a pointer network and reinforcement learning. Our experiments using phrase-based models to simulate Experts to complement neural machine translation models show that the model can be trained to copy the annotations into the output consistently. We demonstrate the benefit of our proposed framework in outof-domain translation scenarios with only lexical resources, improving more than 1.0 BLEU point in both translation directions English to Spanish and German to English