Papers with online learning
Demonstration of a Neural Machine Translation System with Online Learning for Translators (P19-3)
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Miguel Domingo, Mercedes García-Martínez, Amando Estela Pastor, Laurent Bié, Alexander Helle, Álvaro Peris, Francisco Casacuberta, Manuel Herranz Pérez
| Challenge: | a new method of "humanizing" automatic translations has been developed for the translation industry . a demonstration of an online learning system for machine translation in a production environment . |
| Approach: | They present a system which implements online learning for neural machine translation in a production environment. |
| Outcome: | The proposed system saves post-editing effort and adapts to a specific domain or user style. |
Fairness-Aware Online Positive-Unlabeled Learning (2024.emnlp-industry)
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| Challenge: | Positive-unlabeled (PU) learning is a new approach to improve text classification by analyzing the impact of the online setting on fairness. |
| Approach: | They propose to extend Positive-Unlabeled (PU) learning to online learning by analyzing the impact of the online setting on fairness. |
| Outcome: | The proposed approach improves fairness in PU learning in both offline and online settings by using only labeled positive and unlabeled samples. |
BOOM: Beyond Only One Modality KIT’s Multimodal Multilingual Lecture Companion (2026.eacl-demo)
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Sai Koneru, Fabian Retkowski, Christian Huber, Lukas Hilgert, Seymanur Akti, Enes Yavuz Ugan, Alexander Waibel, Jan Niehues
| Challenge: | a multimodal multilingual lecture companion is needed to preserve lecture content in its entirety . globalization of education and rapid growth of online learning have made localizing educational content a challenge . |
| Approach: | They propose a multimodal multilingual lecture companion that translates lecture audio and slides to produce synchronized outputs across three modalities. |
| Outcome: | The proposed solution preserves the original content in its entirety while preserving translations across three modalities. |
Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot (2020.coling-demos)
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| Challenge: | Using conversation with a chatbot, we create annotating and creating datasets through conversation with an open-source platform called Annobot. |
| Approach: | They propose an open-source platform for annotating and creating datasets through conversation with a chatbot. |
| Outcome: | The proposed platform has a wide range of applications including data labelling for binary, multi-class/label classification tasks, preparing data for regression problems and creating sets for issues such as machine translation, question answering or text summarization. |
Towards Incremental Learning of Word Embeddings Using Context Informativeness (P19-2)
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| Challenge: | In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. |
| Approach: | They propose a model that incorporates informativeness into a proposed model of nonce learning, using it for context selection and learning rate modulation. |
| Outcome: | The proposed model is based on a proposed model of nonce learning, and it performs well on the task of learning new words from definitions and potentially uninformative contexts. |
Computational Ad Hominem Detection (P19-2)
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| Challenge: | ad hominem attacks are introduced in debates as an easy win, but their impact on argumentation is limited . a machine learning approach to detect the personal attack is insufficient, we show . |
| Approach: | They propose a machine learning approach that detects ad hominem attacks using social media data . they propose TF-IDF approaches that are insufficient to detect the personal attack . |
| Outcome: | The proposed method has a recall of 80% for a social media data source. |
Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort (2021.acl-long)
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| Challenge: | Existing methods to evaluate multiple systems are expensive and require human evaluators. |
| Approach: | They propose a novel online learning approach that dynamically converges to the top-3 ranked systems for the language pairs considered by taking advantage of human feedback. |
| Outcome: | The proposed approach converges to the top-3 ranked systems for the language pairs considered despite the lack of human feedback for many translations. |
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning (2020.lrec-1)
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| Challenge: | Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model. |
| Approach: | They propose an online learning-based aspect extraction solution that allows users to post-edit the aspect extraction with little effort. |
| Outcome: | The proposed solution dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model. |
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs (2025.acl-long)
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Nicholas E. Corrado, Julian Katz-Samuels, Adithya M Devraj, Hyokun Yun, Chao Zhang, Yi Xu, Yi Pan, Bing Yin, Trishul Chilimbi
| Challenge: | Existing approaches to align large language models rely on large ablation studies, heuristics, or human intuition to produce models with strong performance across tasks. |
| Approach: | They propose an algorithm that mixes datasets during LLM training to balance performance across multiple tasks. |
| Outcome: | The proposed algorithm outperforms existing methods on multitask alignment setups and achieves convergence rate of O(1/T) in the convex case. |
Debiasing Online Preference Learning via Preference Feature Preservation (2025.findings-acl)
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| Challenge: | Recent preference learning frameworks for large language models (LLMs) simplify human preferences with binary pairwise comparisons and scalar rewards. |
| Approach: | They propose a preference feature preservation framework that preserves the distribution of human preference features and maps them throughout the online preference learning process. |
| Outcome: | The proposed framework maintains the distribution of human preference features and utilizing such rich signals throughout the online preference learning process. |
Playpen: An Environment for Exploring Learning From Dialogue Game Feedback (2025.emnlp-main)
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Nicola Horst, Davide Mazzaccara, Antonia Schmidt, Michael Sullivan, Filippo Momentè, Luca Franceschetti, Philipp Sadler, Sherzod Hakimov, Alberto Testoni, Raffaella Bernardi, Raquel Fernández, Alexander Koller, Oliver Lemon, David Schlangen, Mario Giulianelli, Alessandro Suglia
| Challenge: | In this paper, we investigate whether Dialogue Games—goal-directed and rule-governed activities driven predominantly by verbal actions—can also serve as a source of feedback signals for learning. |
| Approach: | They introduce Playpen, an environment for off- and online learning through Dialogue Game self-play, and investigate a representative set of post-training methods: supervised fine-tuning, direct alignment and reinforcement learning with Group Relative Policy Optimization. |
| Outcome: | The proposed model improves performance on unseen instances, but negatively impacts other skills, while interactive learning shows balanced improvements without loss of skills. |