Papers with interactive learning
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (N18-3)
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| Challenge: | End-to-end neural models for conversational agents require large corpus of dialogues to learn effectively. |
| Approach: | They propose a method for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing. |
| Outcome: | The proposed approach can be quickly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users. |
Cooperative Self-training of Machine Reading Comprehension (2022.naacl-main)
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| Challenge: | Pretrained language models provide high-quality contextualized word embeddings, but training question answering models requires large amounts of annotated data for specific domains. |
| Approach: | They propose a framework for automatically generating more non-trivial question-answer pairs to improve model performance. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) pretrained language models and transfer learning approaches on standard question-answering benchmarks. |
Evaluation of Lifelong Learning Systems (2020.lrec-1)
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| Challenge: | Current intelligent systems need the expensive support of machine learning experts to sustain their performance level when used on a daily basis. |
| Approach: | They propose a generic evaluation methodology for lifelong learning systems . they use "initialisation data" to refer to the set of training, development and test data together . |
| Outcome: | The proposed evaluation method is based on the evaluation of human-assisted learning outside the context of lifelong learning. |
INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) absorb static data without the ability to question and refine knowledge. |
| Approach: | They propose a framework in which a “student” LLM engages a ‘teacher’ LLM through iterative inquiries to acquire knowledge across 1,347 contexts. |
| Outcome: | The proposed framework achieves up to 25% improvement in 1,347 contexts across a wide range of scenarios and LLM architectures, with ‘cold-start’ student models matching static learning baselines in as few as five dialogue turns. |
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance (2022.coling-1)
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| Challenge: | Existing methods to learn complex sentence with multiple aspects do not consider correlation between aspects to distinguish overlapped feature. |
| Approach: | They propose a method that uses aspect correlation to improve aspect correlation modeling . they use Recurrent Mechanism to improve the joint representation of aspects . |
| Outcome: | The proposed method is state-of-the-art in multiaspect scenarios. |
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. |