Authors: Quim Motger, Xavier Franch, Jordi Marco

Published on: June 21, 2021

Impact Score: 8.15

Arxiv code: Arxiv:2106.10901

Summary

• What is new: This paper presents a comprehensive survey that aggregates knowledge from various domains, research focuses, and contexts within the field of conversational agents, offering a novel, holistic taxonomy.
• Why this is important: Despite advances in conversational agents, there’s a lack of a generic, context-independent overview that covers all research perspectives in the field.
• What the research proposes: The paper proposes a detailed taxonomy of conversational agents based on a systematic literature review of secondary studies, aiming to provide a clearer understanding and guide future research.
• Results: The research has resulted in a holistic taxonomy that categorizes key dimensions of the conversational agent field, providing valuable insights for researchers.

Technical Details

Technological frameworks used: Systematic literature review

Models used: Deep learning approaches like recurrent neural networks

Data used: Secondary studies across various domains and contexts

Potential Impact

Technology and software companies focusing on human-computer interaction, particularly those developing or employing conversational agents or chatbots.

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