Performance Improvement on Traditional Chinese Task-Oriented Dialogue Systems With Reinforcement Learning and Regularized Dropout Technique

Sheu, Jeng-Shin; Wu, Siang-Ru; Wu, Chin‐Chia

doi:10.1109/access.2023.3248796

Cited by 5 publications

(2 citation statements)

References 36 publications

(49 reference statements)

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“…Applying Reinforcement Learning in the dialog system is the action decision of learning dialogues [9]. The action in the reinforcement learning corresponds to the next steps in the dialogues.…”

Section: Reinforcement Learning Methodsmentioning

confidence: 99%

The Advance of Multi-Round Dialogue System with Deep Learning

Yang

Zhang³

et al. 2023

ACE

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Multi-turn dialog systems have seen significant advances in recent years, driven by various approaches. The Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method have all shown promising results in advancing the state-of-the-art in this field. However, challenges remain in developing models that can handle complex and diverse user inputs and generate responses that are not only informative but also engaging and natural. Further research is needed to address these challenges and advance state of art in multi-turn dialogue systems. This paper reviews four critical methods for improving the quality of multi-turn dialogue systems: Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method. The Multi-layer Semantic Method utilizes multi-layer neural networks to model dialogue context and generate responses with improved coherence and relevance. The reinforcement Learning Method employs a reward-based approach to optimize response generation by training models to maximize long-term dialogue success. The knowledge Graph Method incorporates external knowledge sources, such as knowledge graphs, to enrich the dialogue context and improve response quality. The Medicine Knowledge Graph Method focuses on integrating medical knowledge into dialogue systems for healthcare applications. Each of these methods has demonstrated promising results in enhancing the quality of multi-turn dialogue systems.

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Section: Reinforcement Learning Methodsmentioning

confidence: 99%

The Advance of Multi-Round Dialogue System with Deep Learning

Yang

Zhang³

et al. 2023

ACE

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“…Reinforcement learning (RL) has emerged as a powerful approach for learning state-to-action mappings to achieve goals in various domains [1]- [2], such as conversational voice assistant [3], autonomous vehicles [4] and game [5]- [6]. In the field of communication countermeasure, deep reinforcement learning (DRL) methods have demonstrated their prowess by generating jamming or anti-jamming policies directly from multi-dimensional observation data using large-scale neural networks [7]- [11].…”

Section: Introductionmentioning

confidence: 99%

An Improved Reinforcement Learning Method Based on Unsupervised Learning

Chang,

Li,

Zhang

et al. 2024

IEEE Access

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The approach of directly combining clustering method and reinforcement learning (RL) will lead to encounter the issue where states may have different state transition processes under the same action, resulting in poor policy performance. To address this challenge with multi-dimensional continuous observation data, an improved reinforcement learning method based on unsupervised learning is proposed with a novel framework. Instead of dimensionality reduction methods, unsupervised clustering is employed to indirectly capture the underlying structure of the data. First, the proposed framework incorporates multidimensional information, including the current observation data, the next observation data and reward information, during the clustering process, leading to a more accurate and comprehensive low-dimensional discrete representation of the observation data while retaining preserving transition of Markov decision process. Second, by compressing the observation data into a well-defined state space, the resulting cluster labels serve as the low-dimensional discrete label-states for reinforcement learning to generate more effective and robust policies. Comparative analysis with state-of-the-art RL methods demonstrates that the improved RL methods base on framework achieves higher rewards, indicating its superior performance. Furthermore, the framework exhibits computational efficiency, as evidenced by its reasonable time complexity. This structural innovation allows for better exploration and exploitation of the transition, leading to improved policy performance in engineering applications.

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"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

Islam,

Kabir,

Ghani

et al. 2024

Artif Intell Rev

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Social media is used to categorise products or services, but analysing vast comments is time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews and assessments. However, our approach diverges by offering a thorough analytical perspective with critical analysis, research findings, identified gaps, limitations, challenges and future prospects specific to deep learning-based sentiment analysis in recent times. Furthermore, we provide in-depth investigation into sentiment analysis, categorizing prevalent data, pre-processing methods, text representations, learning models, and applications. We conduct a thorough evaluation of recent advances in deep learning architectures, assessing their pros and cons. Additionally, we offer a meticulous analysis of deep learning methodologies, integrating insights on applied tools, strengths, weaknesses, performance results, research gaps, and a detailed feature-based examination. Furthermore, we present in a thorough discussion of the challenges, drawbacks, and factors contributing to the successful enhancement of accuracy within the realm of sentiment analysis. A critical comparative analysis of our article clearly shows that capsule-based RNN approaches give the best results with an accuracy of 98.02% which is the CNN or RNN-based models. We implemented various advanced deep-learning models across four benchmarks to identify the top performers. Additionally, we introduced the innovative CRDC (Capsule with Deep CNN and Bi structured RNN) model, which demonstrated superior performance compared to other methods. Our proposed approach achieved remarkable accuracy across different databases: IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). Hence, this method holds promise for automated sentiment analysis and potential deployment.

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Performance Improvement on Traditional Chinese Task-Oriented Dialogue Systems With Reinforcement Learning and Regularized Dropout Technique

Cited by 5 publications

References 36 publications

The Advance of Multi-Round Dialogue System with Deep Learning

The Advance of Multi-Round Dialogue System with Deep Learning

An Improved Reinforcement Learning Method Based on Unsupervised Learning

"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

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