Chatbot System Architecture

• URL: http://arxiv.org/abs/2201.06348v1
• Date: Mon, 17 Jan 2022 11:07:58 GMT
• Title: Chatbot System Architecture
• Authors: Moataz Mohammed, Mostafa M. Aref
• Abstract summary: The conversational agents is one of the most interested topics in computer science field in the recent decade. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The conversational agents is one of the most interested topics in computer science field in the recent decade. Which can be composite from more than one subject in this field, which you need to apply Natural Language Processing Concepts and some Artificial Intelligence Techniques such as Deep Learning methods to make decision about how should be the response. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.

Related papers

• Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components [4.262342157729123]
  This study investigates the links between theoretical studies and technical implementations of conversational search systems. We present a layered architecture framework and explain the core functions of conversational search systems. We reflect on our findings in light of the rapid progress in large language models, discussing their capabilities, limitations, and directions for future research.
  arXiv  Detail & Related papers  (2024-07-01T06:24:11Z)
• PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model [79.64376762489164]
  PK-Chat is a Pointer network guided generative dialogue model, incorporating a unified pretrained language model and a pointer network over knowledge graphs. The words generated by PK-Chat in the dialogue are derived from the prediction of word lists and the direct prediction of the external knowledge graph knowledge. Based on the PK-Chat, a dialogue system is built for academic scenarios in the case of geosciences.
  arXiv  Detail & Related papers  (2023-04-02T18:23:13Z)
• From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent [2.899704155417792]
  Social science research states that explanations should be conversational, similar to human-to-human explanations. We show how to incorporate XAI in a conversational agent, using a standard design for the agent. We build upon an XAI question bank, which we extend by quality-controlled paraphrases, to understand the user's information needs.
  arXiv  Detail & Related papers  (2022-09-06T15:01:06Z)
• HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data [87.67278915655712]
  We present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions.
  arXiv  Detail & Related papers  (2022-04-28T00:52:16Z)
• Neural Approaches to Conversational Information Retrieval [94.77863916314979]
  A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface. Recent progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI. This book surveys recent advances in CIR, focusing on neural approaches that have been developed in the last few years.
  arXiv  Detail & Related papers  (2022-01-13T19:04:59Z)
• Few-Shot Bot: Prompt-Based Learning for Dialogue Systems [58.27337673451943]
  Learning to converse using only a few examples is a great challenge in conversational AI. The current best conversational models are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL) We propose prompt-based few-shot learning which does not require gradient-based fine-tuning but instead uses a few examples as the only source of learning.
  arXiv  Detail & Related papers  (2021-10-15T14:36:45Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. This paper reviews the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task. In addition, we categorize dialogue-related pre-training techniques which are employed to enhance PrLMs in dialogue scenarios.
  arXiv  Detail & Related papers  (2021-10-11T03:52:37Z)
• How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus [13.12257465328948]
  We present the Human-Robot Dialogue Learning (HuRDL) Corpus - a novel dialogue corpus collected in an online interactive virtual environment. We describe the corpus data and a corresponding annotation scheme to offer insight into the form and content of questions that humans ask to facilitate learning in a situated environment.
  arXiv  Detail & Related papers  (2021-06-11T16:58:22Z)
• Exploring Recurrent, Memory and Attention Based Architectures for Scoring Interactional Aspects of Human-Machine Text Dialog [9.209192502526285]
  This paper builds on previous work in this direction to investigate multiple neural architectures. We conduct experiments on a conversational database of text dialogs from human learners interacting with a cloud-based dialog system. We find that fusion of multiple architectures performs competently on our automated scoring task relative to expert inter-rater agreements.
  arXiv  Detail & Related papers  (2020-05-20T03:23:00Z)
• Plato Dialogue System: A Flexible Conversational AI Research Platform [64.82999992143448]
  Plato is a flexible Conversational AI platform written in Python that supports any kind of conversational agent architecture. Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
  arXiv  Detail & Related papers  (2020-01-17T18:27:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.