# Active Learning for Interactive Neural Machine Translation of Data Streams

arXiv: https://arxiv.org/abs/1807.11243 · HTML: https://mlsanity.com/paper/1807.11243/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 21 of 21)

- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [Multi-task Active Learning for Pre-trained Transformer-based Models](https://mlsanity.com/paper/2208.05379/) (2022)
- [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)
- [Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations](https://mlsanity.com/paper/2403.04085/) (2024)
- [Active Learning for Abstractive Text Summarization](https://mlsanity.com/paper/2301.03252/) (2023)
- [Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation](https://mlsanity.com/paper/2103.06490/) (2021)
- [Investigating Multi-source Active Learning for Natural Language Inference](https://mlsanity.com/paper/2302.06976/) (2023)
- [Onception: Active Learning with Expert Advice for Real World Machine Translation](https://mlsanity.com/paper/2203.04507/) (2022)
- [Cost-Effective Training in Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2201.05700/) (2022)
- [DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation](https://mlsanity.com/paper/2204.09259/) (2022)
- [Interactive Editing for Text Summarization](https://mlsanity.com/paper/2306.03067/) (2023)
- [Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation](https://mlsanity.com/paper/2205.09661/) (2022)
- [Efficient Machine Translation Corpus Generation](https://mlsanity.com/paper/2306.11838/) (2023)
- [Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation](https://mlsanity.com/paper/2402.19267/) (2024)
- [Domain Adaptation from Scratch](https://mlsanity.com/paper/2209.00830/) (2022)
- [Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2301.00688/) (2022)
- [Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages](https://mlsanity.com/paper/2305.03873/) (2023)
- [Segment-Based Interactive Machine Translation for Pre-trained Models](https://mlsanity.com/paper/2407.06990/) (2024)
- [Efficient Machine Translation Corpus Generation: Integrating Human-in-the-Loop Post-Editing with Large Language Models](https://mlsanity.com/paper/2502.12755/) (2025)
- [Verb-ICL: Rethinking In-Context Learning for Structured Prediction](https://mlsanity.com/paper/2610.04725/) (2026)

## Abstract (arXiv)

We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model. We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin.