# Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages

arXiv: https://arxiv.org/abs/2108.07127 · HTML: https://mlsanity.com/paper/2108.07127/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Dual Learning for Machine Translation](https://mlsanity.com/paper/1611.00179/) (2016)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism](https://mlsanity.com/paper/1601.01073/) (2016)
- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Meta-Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1808.08437/) (2018)
- [Rapid Adaptation of Neural Machine Translation to New Languages](https://mlsanity.com/paper/1808.04189/) (2018)
- [Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder](https://mlsanity.com/paper/1611.04798/) (2016)
- [Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information](https://mlsanity.com/paper/2010.03142/) (2020)
- [Multi-Source Neural Translation](https://mlsanity.com/paper/1601.00710/) (2016)
- [Multilingual Language Processing From Bytes](https://mlsanity.com/paper/1512.00103/) (2015)
- [Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation](https://mlsanity.com/paper/1804.07878/) (2018)
- [Paraphrases as Foreign Languages in Multilingual Neural Machine Translation](https://mlsanity.com/paper/1808.08438/) (2018)
- [Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation](https://mlsanity.com/paper/2104.05848/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2403.09259/) (2024)
- [Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages](https://mlsanity.com/paper/2305.03873/) (2023)
- [Massively Multilingual Text Translation For Low-Resource Languages](https://mlsanity.com/paper/2401.16582/) (2024)

## Abstract (arXiv)

We translate a closed text that is known in advance and available in many languages into a new and severely low resource language. Most human translation efforts adopt a portion-based approach to translate consecutive pages/chapters in order, which may not suit machine translation. We compare the portion-based approach that optimizes coherence of the text locally with the random sampling approach that increases coverage of the text globally. Our results show that the random sampling approach performs better. When training on a seed corpus of ~1,000 lines from the Bible and testing on the rest of the Bible (~30,000 lines), random sampling gives a performance gain of +11.0 BLEU using English as a simulated low resource language, and +4.9 BLEU using Eastern Pokomchi, a Mayan language. Furthermore, we compare three ways of updating machine translation models with increasing amount of human post-edited data through iterations. We find that adding newly post-edited data to training after vocabulary update without self-supervision performs the best. We propose an algorithm for human and machine to work together seamlessly to translate a closed text into a severely low resource language.