Unstructured Text Enhanced Open-Domain Dialogue System: A Systematic Survey

Ma, Longxuan; LiMingda,; Zhang, Weinan; LiJiapeng,; LiuTing,

doi:10.1145/3464377

Cited by 17 publications

(6 citation statements)

References 108 publications

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“…Although early dialogue systems made some progress in specific environments, they had limited effectiveness in a wide range of scenarios. In recent times, there has been a concerted effort to enhance the anthropomorphic characteristics of robots, and researchers have focused on open-domain human-machine dialogue systems, resulting in the emergence of many valuable human-machine dialogue models [6,7].…”

Section: Related Workmentioning

confidence: 99%

A Mood Semantic Awareness Model for Emotional Interactive Robots

Zhou,

Yu,

Wang

et al. 2024

Sensors

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The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human–machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users’ degree of interest are formulated. By evaluating the degree of interest, the system can determine whether it should transition to a new topic to maintain the user’s interest. This model can also address issues such as overly purposeful responses and rigid emotional expressions in generated replies. Simultaneously, this study explores topic continuation after answering a question, the construction of dialogue rounds, keyword counting, and the creation of a target text similarity matrix for each text in the dialogue dataset. The matrix is normalized, weights are assigned, and the final text score is calculated. In the text with the highest score, the content of dialogue continuation is determined by calculating a subsequent sentence with the highest similarity. This resolves the issue in which the conversational bot fails to continue dialogue on a topic after answering a question, instead waiting for the user to voluntarily provide more information, resulting in topic interruption. As described in the experimental section, both automatic and manual evaluations were conducted to validate the significant improvement in the mood semantic awareness model’s performance in terms of dialogue quality and user experience.

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Section: Related Workmentioning

confidence: 99%

A Mood Semantic Awareness Model for Emotional Interactive Robots

Zhou,

Yu,

Wang

et al. 2024

Sensors

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“…There have been many different approaches to open-domain dialogue [29]. Recent work has seen an increased emphasis on trained end-to-end conversational systems [40,41,37,7].…”

Section: Related Workmentioning

confidence: 99%

Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize

Bowden¹,

Walker²

2023

Preprint

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There has been an increased focus on creating conversational opendomain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive improvements in Natural Language Understanding (NLU) and the growth of available knowledge resources can partially support a robust conversation, these conversations generally lack the rapport between two humans that know each other. We developed a robust open-domain conversational system, Athena, that real Amazon Echo users access and evaluate at scale in the context of the Alexa Prize competition. We experiment with methods intended to increase intimacy between Athena and the user by heuristically developing a rule-based user model that personalizes both the current and subsequent conversations and evaluating specific personal opinion question strategies in A/B studies. Our results show a statistically significant positive impact on perceived conversation quality and length when employing these strategies.

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“…Most KGC models utilize an attention mechanism [29] and a memory network framework [30] to dynamically read the document memory built by the encoder and knowledge selection modules. Following Ma et al's [31] classification system, we categorize the knowledge selection module into soft selection or hard selection, depending on the existence of a sampling mechanism that explicitly selects the most relevant knowledge snippet among the candidates.…”

Section: Related Workmentioning

confidence: 99%

Retrieval-Augmented Response Generation for Knowledge-Grounded Conversation in the Wild

Ahn¹,

Lee

Shim

et al. 2022

IEEE Access

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Users on the internet usually have conversations on interesting facts or topics along with diverse knowledge from the web. However, most existing knowledge-grounded conversation models consider only a single document regarding the topic of a conversation. The recently proposed retrievalaugmented models generate a response based on multiple documents; however, they ignore the given topic and use only the local context of the conversation. To this end, we introduce a novel retrieval-augmented response generation model that retrieves an appropriate range of documents relevant to both the topic and local context of a conversation and uses them for generating a knowledge-grounded response. Our model first accepts both topic words extracted from the whole conversation and the tokens before the response to yield multiple representations. It then chooses representations of the first N token and ones of keywords from the conversation and document encoders and compares the two groups of representation from the conversation with those groups of the document, respectively. For training, we introduce a new dataweighting scheme to encourage the model to produce knowledge-grounded responses without ground truth knowledge. Both automatic and human evaluation results with a large-scale dataset show that our models can generate more knowledgeable, diverse, and relevant responses compared to the state-of-the-art models.

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Unstructured Text Enhanced Open-Domain Dialogue System: A Systematic Survey

Cited by 17 publications

References 108 publications

A Mood Semantic Awareness Model for Emotional Interactive Robots

A Mood Semantic Awareness Model for Emotional Interactive Robots

Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize

Retrieval-Augmented Response Generation for Knowledge-Grounded Conversation in the Wild

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