EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only
Abstract: This paper presents EvolveMT for efficiently combining multiple machine translation (MT) engines. The proposed system selects the output from a single engine for each segment by utilizing online learning techniques to predict the most suitable system for every translation request. A neural quality estimation metric supervises the method without requiring reference translations. The online learning capability of this system allows for dynamic adaptation to alterations in the domain or machine translation engines, thereby obviating the necessity for additional training. EvolveMT selects a subset of translation engines to be called based on the source sentence features. The degree of exploration is configurable according to the desired quality-cost trade-off. Results from custom datasets demonstrate that EvolveMT achieves similar translation accuracy at a lower cost than selecting the best translation of each segment from all translations using an MT quality estimator. To our knowledge, EvolveMT is the first meta MT system that adapts itself after deployment to incoming translation requests from the production environment without needing costly retraining on human feedback.
- Mikko Aulamo and Jörg Tiedemann. 2019. The OPUS resource repository: An open package for creating parallel corpora and machine translation services. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 389–394, Turku, Finland. Linköping University Electronic Press.
- Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116.
- Experts, errors, and context: A large-scale study of human evaluation for machine translation. Transactions of the Association for Computational Linguistics, 9:1460–1474.
- Results of the wmt21 metrics shared task: Evaluating metrics with expert-based human evaluations on ted and news domain. In Proceedings of the Sixth Conference on Machine Translation, pages 733–774.
- cushlepor: customising hlepor metric using optuna for higher agreement with human judgments or pre-trained language model labse. In Proceedings of the Sixth Conference on Machine Translation, pages 1014–1023.
- Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear.
- Pierre Lison and Jörg Tiedemann. 2016. OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 923–929, Portorož, Slovenia. European Language Resources Association (ELRA).
- Results of the wmt20 metrics shared task. In Proceedings of the Fifth Conference on Machine Translation, pages 688–725.
- Onception: Active learning with expert advice for real world machine translation. arXiv preprint arXiv:2203.04507.
- Machine translation system selection from bandit feedback. arXiv preprint arXiv:2002.09646.
- Stanza: A python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 101–108, Online. Association for Computational Linguistics.
- COMET: A neural framework for MT evaluation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2685–2702, Online. Association for Computational Linguistics.
- Regressive ensemble for machine translation quality evaluation. arXiv preprint arXiv:2109.07242.
- Democratizing machine translation with opus-mt.
- Flaml: a fast and lightweight automl library. Proceedings of Machine Learning and Systems, 3:434–447.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.