# To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation

arXiv: https://arxiv.org/abs/2403.09259 · HTML: https://mlsanity.com/paper/2403.09259/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://mlsanity.com/paper/2310.18018/) (2023)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Cold-start Active Learning through Self-supervised Language Modeling](https://mlsanity.com/paper/2010.09535/) (2020)
- [Balancing Training for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2004.06748/) (2020)
- [Curriculum Learning for Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/1905.05816/) (2019)
- [PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection](https://mlsanity.com/paper/2103.00128/) (2021)
- [Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates](https://mlsanity.com/paper/2101.08133/) (2021)
- [Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings](https://mlsanity.com/paper/1908.10430/) (2019)
- [Phrase-level Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2106.11375/) (2021)
- [Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation](https://mlsanity.com/paper/1410.5877/) (2014)
- [Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation](https://mlsanity.com/paper/1907.02326/) (2019)
- [Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets](https://mlsanity.com/paper/1409.4835/) (2014)
- [Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages](https://mlsanity.com/paper/2108.07127/) (2021)

## Built on by (most cited first, 8 of 8)

- [Language Model-Driven Data Pruning Enables Efficient Active Learning](https://mlsanity.com/paper/2410.04275/) (2024)
- [CrowdAgent: Multi-Agent Managed Multi-Source Annotation System](https://mlsanity.com/paper/2509.14030/) (2025)
- [EDGE: Efficient Data Selection for LLM Agents via Guideline Effectiveness](https://mlsanity.com/paper/2502.12494/) (2025)
- [Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding](https://mlsanity.com/paper/2507.09758/) (2025)
- [You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models](https://mlsanity.com/paper/2509.14031/) (2025)
- [ATGen: A Framework for Active Text Generation](https://mlsanity.com/paper/2506.23342/) (2025)
- [Loss-Based Active Learning for Neural Abstractive Summarization](https://mlsanity.com/paper/2608.25881/) (2026)
- [Verb-ICL: Rethinking In-Context Learning for Structured Prediction](https://mlsanity.com/paper/2610.04725/) (2026)

## Abstract (arXiv)

Active learning (AL) techniques reduce labeling costs for training neural machine translation (NMT) models by selecting smaller representative subsets from unlabeled data for annotation. Diversity sampling techniques select heterogeneous instances, while uncertainty sampling methods select instances with the highest model uncertainty. Both approaches have limitations - diversity methods may extract varied but trivial examples, while uncertainty sampling can yield repetitive, uninformative instances. To bridge this gap, we propose Hybrid Uncertainty and Diversity Sampling (HUDS), an AL strategy for domain adaptation in NMT that combines uncertainty and diversity for sentence selection. HUDS computes uncertainty scores for unlabeled sentences and subsequently stratifies them. It then clusters sentence embeddings within each stratum and computes diversity scores by distance to the centroid. A weighted hybrid score that combines uncertainty and diversity is then used to select the top instances for annotation in each AL iteration. Experiments on multi-domain German-English and French-English datasets demonstrate the better performance of HUDS over other strong AL baselines. We analyze the sentence selection with HUDS and show that it prioritizes diverse instances having high model uncertainty for annotation in early AL iterations.