# Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation

arXiv: https://arxiv.org/abs/1410.5877 · HTML: https://mlsanity.com/paper/1410.5877/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2014.

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

- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [Active Imitation Learning with Noisy Guidance](https://mlsanity.com/paper/2005.12801/) (2020)
- [Phrase-level Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2106.11375/) (2021)
- [COMET-QE and Active Learning for Low-Resource Machine Translation](https://mlsanity.com/paper/2210.15696/) (2022)
- [To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2403.09259/) (2024)
- [Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation](https://mlsanity.com/paper/2103.06490/) (2021)
- [Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts](https://mlsanity.com/paper/2405.13541/) (2024)
- [Impact of Stop Sets on Stopping Active Learning for Text Classification](https://mlsanity.com/paper/2201.05460/) (2022)
- [Active Learning for NLP with Large Language Models](https://mlsanity.com/paper/2401.07367/) (2024)
- [Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages](https://mlsanity.com/paper/2305.03873/) (2023)

## Abstract (arXiv)

We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement.