# Domain Adaptive Inference for Neural Machine Translation

arXiv: https://arxiv.org/abs/1906.00408 · HTML: https://mlsanity.com/paper/1906.00408/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

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

- [Learning to Decode Collaboratively with Multiple Language Models](https://mlsanity.com/paper/2403.03870/) (2024)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation](https://mlsanity.com/paper/2103.13678/) (2021)
- [Continual Knowledge Distillation for Neural Machine Translation](https://mlsanity.com/paper/2212.09097/) (2022)
- [Instruction-tuned Large Language Models for Machine Translation in the Medical Domain](https://mlsanity.com/paper/2408.16440/) (2024)
- [Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions](https://mlsanity.com/paper/2210.05059/) (2022)
- [Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation](https://mlsanity.com/paper/2109.06308/) (2021)
- [F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translation](https://mlsanity.com/paper/2404.04846/) (2024)
- [Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring](https://mlsanity.com/paper/2606.29026/) (2026)
- [BERAG: Bayesian Ensemble Retrieval-Augmented Generation for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2604.22678/) (2026)

## Abstract (arXiv)

We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain. We adapt sequentially across two Spanish-English and three English-German tasks, comparing unregularized fine-tuning, L2 and Elastic Weight Consolidation. We then report a novel scheme for adaptive NMT ensemble decoding by extending Bayesian Interpolation with source information, and show strong improvements across test domains without access to the domain label.