# Domain Adaptation of Machine Translation with Crowdworkers

arXiv: https://arxiv.org/abs/2210.15861 · HTML: https://mlsanity.com/paper/2210.15861/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [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)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [TICO-19: the Translation Initiative for Covid-19](https://mlsanity.com/paper/2007.01788/) (2020)
- [Domain Robustness in Neural Machine Translation](https://mlsanity.com/paper/1911.03109/) (2019)
- [A Survey of Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1806.00258/) (2018)
- [Dynamic Data Selection for Neural Machine Translation](https://mlsanity.com/paper/1708.00712/) (2017)
- [JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus](https://mlsanity.com/paper/1911.10668/) (2019)
- [Exploiting Sentence Order in Document Alignment](https://mlsanity.com/paper/2004.14523/) (2020)
- [Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance](https://mlsanity.com/paper/2002.00761/) (2020)
- [Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data](https://mlsanity.com/paper/1809.00196/) (2018)
- [Measuring Motivations of Crowdworkers: The Multidimensional Crowdworker Motivation Scale](https://mlsanity.com/paper/1702.01661/) (2017)

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

- [A Japanese-Chinese Parallel Corpus Using Crowdsourcing for Web Mining](https://mlsanity.com/paper/2405.09017/) (2024)
- [Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization](https://mlsanity.com/paper/2410.02628/) (2024)
- [Toward Machine Interpreting: Lessons from Human Interpreting Studies](https://mlsanity.com/paper/2508.07964/) (2025)

## Abstract (arXiv)

Although a machine translation model trained with a large in-domain parallel corpus achieves remarkable results, it still works poorly when no in-domain data are available. This situation restricts the applicability of machine translation when the target domain's data are limited. However, there is great demand for high-quality domain-specific machine translation models for many domains. We propose a framework that efficiently and effectively collects parallel sentences in a target domain from the web with the help of crowdworkers. With the collected parallel data, we can quickly adapt a machine translation model to the target domain. Our experiments show that the proposed method can collect target-domain parallel data over a few days at a reasonable cost. We tested it with five domains, and the domain-adapted model improved the BLEU scores to +19.7 by an average of +7.8 points compared to a general-purpose translation model.