Papers with reinforcement learning method
SCoPE VLM: Selective Context Processing for Efficient Document Navigation in Vision-Language Models (2026.eacl-long)
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| Challenge: | Existing methods for document understanding are memory-intensive and impractical for local deployments. |
| Approach: | They propose a document navigation expert that leverages a Chain of Scroll mechanism to selectively and recursively navigate documents, focusing exclusively on relevant segments. |
| Outcome: | The proposed method reduces memory usage and effectively models human-like reading behaviors. |
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness (2022.naacl-main)
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| Challenge: | Existing approaches to Multi-document summarization are limited due to the extremely long input length. |
| Approach: | They propose an extract-then-abstract Transformer framework to overcome the problem . they leverage pre-trained language models to construct hierarchical extractors and abstractors . |
| Outcome: | The proposed framework outperforms baseline models with comparable model sizes and achieves the best results on the Multi-News, Multi-XScience, and WikiCatSum corpora. |
Unsupervised Context Rewriting for Open Domain Conversation (D19-1)
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| Challenge: | Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency. |
| Approach: | They propose a context rewriting method which explicitly rewrites the last utterance by considering context history. |
| Outcome: | The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots. |
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation (2020.findings-emnlp)
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Toru Nishino, Ryota Ozaki, Yohei Momoki, Tomoki Taniguchi, Ryuji Kano, Norihisa Nakano, Yuki Tagawa, Motoki Taniguchi, Tomoko Ohkuma, Keigo Nakamura
| Challenge: | Medical datasets are imbalanced in their finding labels because incidence rates differ among diseases . authors propose a novel reinforcement learning method with a reconstructor to improve clinical correctness of generated reports. |
| Approach: | They propose a reinforcement learning method with a reconstructor to improve clinical correctness of generated reports. |
| Outcome: | The proposed method improves clinical correctness of generated reports . it also trains the model on infrequent findings . |
Guided Dialogue Policy Learning without Adversarial Learning in the Loop (2020.findings-emnlp)
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Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao
| Challenge: | Reinforcement learning methods suffer from sparse and unstable reward signals . alternating training of dialogue agent and reward model can get stuck in local optima . |
| Approach: | They propose to decompose adversarial training into two steps to improve dialogue policy learning. |
| Outcome: | The proposed method achieves remarkable task success rate using both on-policy and off-poly reinforcement learning methods. |
F2RL: Factuality and Faithfulness Reinforcement Learning Framework for Claim-Guided Evidence-Supported Counterspeech Generation (2024.emnlp-main)
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| Challenge: | Existing methods for generating evidence-supported counterspeech lack clear guidance with a core claim for organizing evidence. |
| Approach: | They propose a Factuality and Faithfulness Reinforcement Learning framework for generating claim-guided and evidence-supported counterspeech (F2RL) they generate counter-claims based on hate speech and design a self-evaluation mechanism to select the most appropriate one. |
| Outcome: | The proposed framework achieves excellent performance on three benchmark datasets with strong factuality and faithfulness. |
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation (D18-1)
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| Challenge: | Existing models for narrative story generation lack semantic dependency among sentences. |
| Approach: | They propose a skeleton-based model that generates the most critical phrases and expands them to a complete sentence. |
| Outcome: | The proposed model can generate significantly more coherent stories according to human evaluation and automatic evaluation. |
Towards Better Question Generation in QA-based Event Extraction (2024.findings-acl)
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| Challenge: | True. True. EE aims to extract event-related information from unstructured texts. |
| Approach: | They propose a reinforcement learning method that evaluates the quality of a question and provides clear guidance to QA models. |
| Outcome: | The proposed method generates generalizable, high-quality, and context-dependent questions and provides clear guidance to QA models. |
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting (2021.emnlp-main)
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| Challenge: | Existing methods focus on reasoning at past timestamps to complete the missing facts, and there are only a few works of reasoning on known TKGs to forecast future facts. |
| Approach: | They propose a time-shaped reward method that captures historical knowledge graph snapshots and a new representation method for unseen entities to improve the inductive inference ability of the model. |
| Outcome: | The proposed method improves on four benchmark datasets with higher explainability, less calculation, and fewer parameters when compared with existing state-of-the-art methods. |