Neural Conversation Model Controllable by Given Dialogue Act Based on Adversarial Learning and Label-aware Objective

Kawano, Seiya; Yoshino, Katsumi; Nakamura, Satoshi

doi:10.18653/v1/w19-8627

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…Hierarchical attention [43] or a copying mechanism [44] may explicitly solve this problem based on the word information in dialogue contexts. Incorporating such different aspects of entrainment as dialogue act choice is also important [8], [27].…”

Section: Discussionmentioning

confidence: 99%

“…However, model optimization based on existing cross-entropy loss does not satisfactorily control the generation because the optimization is calculated word-by-word. In contrast, optimization based on reinforcement learning has the potential to train such a controllable response generation model [27]. Thus, we introduce the REINFORCE algorithm, which is based on reinforcement learning [15], [19].…”

Section: Model Optimization To Entrainment Degree Based On Reinforcement Learningmentioning

confidence: 99%

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Entrainable Neural Conversation Model Based on Reinforcement Learning

et al. 2020

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The synchronization of words in conversation, called entrainment, is generally observed in human-human conversations. Entrainment has a high correlation with dialogue success, naturalness, and engagement. In this paper, we define entrainment scores based on the word similarities in semantic space to evaluate the entrainment of system generation. We optimized a neural conversation model to the entrainment scores using reinforcement learning so that the system can control the degree of entrainment of the system response. Experimental results showed that the proposed entrainable neural conversation model generated comparable or more natural responses than conventional models and satisfactorily controlled the degree of entrainment of the generated responses.

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Section: Discussionmentioning

confidence: 99%

Section: Model Optimization To Entrainment Degree Based On Reinforcement Learningmentioning

confidence: 99%

Entrainable Neural Conversation Model Based on Reinforcement Learning

et al. 2020

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“…Machine learning based systems respond to a user's message by generating new response text after being trained on large corpora. A recent and popular approach is to train these systems end-to-end using neural network architectures, such as Seq2Seq [22][23][24] or GAN [25,26]. This idea was first proposed by [17] using phrase-based machine translation to translate a user's message into a system response.…”

Section: Related Workmentioning

confidence: 99%

Contextual Modeling in Context-Aware Conversation Systems

2023

KSII TIIS

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Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

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“…Raheja and Tetreault (2019); Ahmadvand et al (2019) constructed a model that classifies a DA for an utterance. Kawano et al (2019) proposed a model to generate responses with a specified DA. This was achieved through adversarial learning.…”

Section: Related Workmentioning

confidence: 99%

Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation

Sakaeda¹,

Kawahara²

2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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We aim to overcome the lack of diversity in responses of current dialogue systems and to develop a dialogue system that is engaging as a conversational partner. We propose a generator-evaluator model that evaluates multiple responses generated by a response generator and selects the best response by an evaluator. By generating multiple responses, we obtain diverse responses. We conduct human evaluations to compare the output of the proposed system with that of a baseline system. The results of the human evaluations showed that the proposed system's responses were often judged to be better than the baseline system's, and indicated the effectiveness of the proposed method.

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Neural Conversation Model Controllable by Given Dialogue Act Based on Adversarial Learning and Label-aware Objective

Cited by 6 publications

References 28 publications

Entrainable Neural Conversation Model Based on Reinforcement Learning

Entrainable Neural Conversation Model Based on Reinforcement Learning

Contextual Modeling in Context-Aware Conversation Systems

Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation

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