Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
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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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2014.
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Citing papers per year: 2020: 1; 2021: 2; 2022: 3; 2023: 1; 2024: 3.
10 papers.
- 2022A Survey of Active Learning for Natural Language Processingcited by 40
- 2020Active Imitation Learning with Noisy Guidancecited by 15
- 2021Phrase-level Active Learning for Neural Machine Translationcited by 13
- 2022COMET-QE and Active Learning for Low-Resource Machine Translationcited by 10
- 2024To Label or Not to Label: Hybrid Active Learning for Neural Machine Translationcited by 7
- 2021Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translationcited by 6
- 2024Annotation-Efficient Language Model Alignment via Diverse and Representative Response Textscited by 3
- 2022Impact of Stop Sets on Stopping Active Learning for Text Classificationcited by 2
- 2024Active Learning for NLP with Large Language Modelscited by 2
- 2023Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languagescited by 0
- 2024Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts22 May 2024
- 2024To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation14 Mar 2024
- 2024Active Learning for NLP with Large Language Models14 Jan 2024
- 2023Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages5 May 2023
- 2022COMET-QE and Active Learning for Low-Resource Machine Translation27 Oct 2022
- 2022A Survey of Active Learning for Natural Language Processing18 Oct 2022
- 2022Impact of Stop Sets on Stopping Active Learning for Text Classification8 Jan 2022
- 2021Phrase-level Active Learning for Neural Machine Translation21 Jun 2021
- 2021Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation11 Mar 2021
- 2020Active Imitation Learning with Noisy Guidance26 May 2020