TY - GEN
T1 - State Machine Based Human-Bot Conversation Model and Services
AU - Zamanirad, Shayan
AU - Benatallah, Boualem
AU - Rodriguez, Carlos
AU - Yaghoubzadehfard, Mohammadali
AU - Bouguelia, Sara
AU - Brabra, Hayet
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Task-oriented virtual assistants (or simply chatbots) are in very high demand these days. They employ third-party APIs to serve end-users via natural language interactions. Chatbots are famed for their easy-to-use interface and gentle learning curve (it only requires one of humans’ most innate ability, the use of natural language). Studies on human conversation patterns show, however, that day-to-day dialogues are of multi-turn and multi-intent nature, which pushes the need for chatbots that are more resilient and flexible to this style of conversations. In this paper, we propose the idea of leveraging Conversational State Machine to make it a core part of chatbots’ conversation engine by formulating conversations as a sequence of states. Here, each state covers an intent and contains a nested state machine to help manage tasks associated to the conversation intent. Such enhanced conversation engine, together with a novel technique to spot implicit information from dialogues (by exploiting Dialog Acts), allows chatbots to manage tangled conversation situations where most existing chatbot technologies fail.
AB - Task-oriented virtual assistants (or simply chatbots) are in very high demand these days. They employ third-party APIs to serve end-users via natural language interactions. Chatbots are famed for their easy-to-use interface and gentle learning curve (it only requires one of humans’ most innate ability, the use of natural language). Studies on human conversation patterns show, however, that day-to-day dialogues are of multi-turn and multi-intent nature, which pushes the need for chatbots that are more resilient and flexible to this style of conversations. In this paper, we propose the idea of leveraging Conversational State Machine to make it a core part of chatbots’ conversation engine by formulating conversations as a sequence of states. Here, each state covers an intent and contains a nested state machine to help manage tasks associated to the conversation intent. Such enhanced conversation engine, together with a novel technique to spot implicit information from dialogues (by exploiting Dialog Acts), allows chatbots to manage tangled conversation situations where most existing chatbot technologies fail.
KW - Conversational chatbot
KW - Natural language processing
KW - REST API
KW - State machine
UR - https://www.scopus.com/pages/publications/85086265910
U2 - 10.1007/978-3-030-49435-3_13
DO - 10.1007/978-3-030-49435-3_13
M3 - Conference contribution
AN - SCOPUS:85086265910
SN - 9783030494346
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 199
EP - 214
BT - Advanced Information Systems Engineering - 32nd International Conference, CAiSE 2020, Proceedings
A2 - Dustdar, Schahram
A2 - Yu, Eric
A2 - Pant, Vik
A2 - Salinesi, Camille
A2 - Rieu, Dominique
PB - Springer
T2 - 32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020
Y2 - 8 June 2020 through 12 June 2020
ER -