# DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation

arXiv: https://arxiv.org/abs/2204.09259 · HTML: https://mlsanity.com/paper/2204.09259/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Simple, Scalable Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1909.08478/) (2019)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Scaling Laws for Neural Machine Translation](https://mlsanity.com/paper/2109.07740/) (2021)
- [Predicting Performance for Natural Language Processing Tasks](https://mlsanity.com/paper/2005.00870/) (2020)
- [Fast Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1612.06897/) (2016)
- [Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/2103.02262/) (2021)
- [Towards More Fine-grained and Reliable NLP Performance Prediction](https://mlsanity.com/paper/2102.05486/) (2021)
- [Dynamic Data Selection and Weighting for Iterative Back-Translation](https://mlsanity.com/paper/2004.03672/) (2020)
- [Naver Labs Europe's Systems for the WMT19 Machine Translation Robustness Task](https://mlsanity.com/paper/1907.06488/) (2019)
- [Active Learning for Interactive Neural Machine Translation of Data Streams](https://mlsanity.com/paper/1807.11243/) (2018)
- [Learning a Multi-Domain Curriculum for Neural Machine Translation](https://mlsanity.com/paper/1908.10940/) (2019)
- [ASR Performance Prediction on Unseen Broadcast Programs using Convolutional Neural Networks](https://mlsanity.com/paper/1804.08477/) (2018)
- [Translation Transformers Rediscover Inherent Data Domains](https://mlsanity.com/paper/2109.07864/) (2021)

## Built on by (most cited first, 2 of 2)

- [m4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter](https://mlsanity.com/paper/2210.11912/) (2022)
- [Domain Curricula for Code-Switched MT at MixMT 2022](https://mlsanity.com/paper/2210.17463/) (2022)

## Abstract (arXiv)

Domain Adaptation (DA) of Neural Machine Translation (NMT) model often relies on a pre-trained general NMT model which is adapted to the new domain on a sample of in-domain parallel data. Without parallel data, there is no way to estimate the potential benefit of DA, nor the amount of parallel samples it would require. It is however a desirable functionality that could help MT practitioners to make an informed decision before investing resources in dataset creation. We propose a Domain adaptation Learning Curve prediction (DaLC) model that predicts prospective DA performance based on in-domain monolingual samples in the source language. Our model relies on the NMT encoder representations combined with various instance and corpus-level features. We demonstrate that instance-level is better able to distinguish between different domains compared to corpus-level frameworks proposed in previous studies. Finally, we perform in-depth analyses of the results highlighting the limitations of our approach, and provide directions for future research.