Papers with DRL
Deep Reinforcement Learning for NLP (P18-5)
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| Challenge: | Many natural language processing tasks can be formulated as deep reinforcement learning (DRL) problems. |
| Approach: | This tutorial provides an introduction to the foundations of deep reinforcement learning . it describes recent advances in designing deep reinforcement for NLP . |
| Outcome: | This tutorial provides an introduction to the foundations of deep reinforcement learning and some practical solutions for NLP tasks. |
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games (2022.acl-long)
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| Challenge: | Text-based games provide an interactive way to study natural language processing. |
| Approach: | They propose a two-phase training framework to decouple language learning from reinforcement learning and improve the sample efficiency. |
| Outcome: | The proposed method significantly improves performance and sample efficiency against compound error and limited pre-training data. |
Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems (2020.findings-emnlp)
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| Challenge: | In order to improve the sample-efficiency of deep reinforcement learning, we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS). |
| Approach: | They propose to use an actor-double-critic to improve the stability and overall performance of imagination augmented agent (I2A) in spoken dialogue systems. |
| Outcome: | The proposed model-based agent (ADC) improves the stability and sample-efficiency of deep reinforcement learning (DRL) on a restaurant booking task. |
Structured Dialogue Policy with Graph Neural Networks (C18-1)
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| Challenge: | Recent advances focus on improving DRL-based dialogue policy optimization. |
| Approach: | They propose to design a graph neural network structure that is better suited for dialogue management. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in 18 tasks of the PyDial benchmark. |
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System (C18-1)
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| Challenge: | Existing systems for general sequence tagging/labeling are based on neural network architectures. |
| Approach: | They propose a deep neural network based sequence labeling model and a augmented tagger to improve system performance by modeling the data with minority tags. |
| Outcome: | The proposed system outperforms the current state-of-the-art model on ATIS and CoNLL-2003 datasets by 1.9% and 1.4%. |
High-Quality Dialogue Diversification by Intermittent Short Extension Ensembles (2021.findings-acl)
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| Challenge: | Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully. |
| Approach: | They propose a method to diversify dialogues using a set of user models by constraining the intensity to interact with diverse user models. |
| Outcome: | The proposed method improves the performance of several state-of-the-art DRL dialogue agents trained in simulators. |
An Efficient Task-Oriented Dialogue Policy: Evolutionary Reinforcement Learning Injected by Elite Individuals (2025.acl-long)
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| Challenge: | Evolutionary Algorithms (EAs) have been proven to effectively explore the solution space of neural networks by maintaining population diversity. |
| Approach: | They propose an elite individual injection mechanism to enhance EA’s search efficiency by adaptively introducing best-performing individuals into the population. |
| Outcome: | Experiments on four datasets show that the proposed approach significantly improves the balance between exploration and exploitation, boosting performance. |
Deep Reinforcement Learning-based Dialogue Policy with Graph Convolutional Q-network (2024.lrec-main)
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| Challenge: | Existing methods for deep reinforcement learning lack the ability to learn the relationship between dialogue states and actions. |
| Approach: | They propose a graph-structured dialogue policy framework for task-oriented dialogue systems that uses bipartite graphs to construct two different bipartites and generate user-related and knowledge-related subgraphs. |
| Outcome: | The proposed framework significantly improves the effectiveness and stability of dialogue policies. |
An Efficient Dialogue Policy Agent with Model-Based Causal Reinforcement Learning (2025.coling-main)
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| Challenge: | Existing models for dialogue policy training consider one-step dialogues, leading to inaccurate simulations. |
| Approach: | They propose a framework for dialogue policy learning that trains an agent to select dialogue actions via deep reinforcement learning. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three dialogue datasets . it uses model-based reinforcement learning with automatically constructed causal chains . |
GeoDRL: A Self-Learning Framework for Geometry Problem Solving using Reinforcement Learning in Deductive Reasoning (2023.findings-acl)
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| Challenge: | Existing methods for automated geometry problem solving lack labeled data. |
| Approach: | They propose a framework that integrates logic graph deduction and deep reinforcement learning to optimize geometry reasoning as a Markov Decision Process. |
| Outcome: | The proposed framework improves accuracy and interpretability in the Geometry3K dataset while maintaining correctness. |
Semantic-Aware Action Space Compression via LLM-DRL Synergy for Efficient Task-oriented Dialogue Policy Exploration (2025.findings-emnlp)
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| Challenge: | Pre-trained large language models (LLMs) with world knowledge and semantic understanding are promising for task-oriented dialogue systems. |
| Approach: | a framework that synergizes pre-trained large language models with DRL is proposed . a lightweight action pruning mechanism is employed to eliminate implausible actions . |
| Outcome: | a new framework synergizes pre-trained large language models with DRL to guide decision-making . the proposed framework eliminates semantically implausible or low-potential actions from multi-turn dialogue context . |