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Topic

Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.


Papers
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Book ChapterDOI
Danning Zheng1, Ruihua Song2, Tianran Hu1, Hao Fu1, Jin Zhou1 
28 Jun 2019
TL;DR: This work first designs a metaphor generation framework, which generates topic-aware and novel figurative sentences, and evaluates the effects of employing metaphors in human-chatbot conversations.
Abstract: As the wide adoption of intelligent chatbot in human daily life, user demands for such systems evolve from basic task-solving conversations to more casual and friend-like communication. To meet the user needs and build emotional bond with users, it is essential for social chatbots to incorporate more human-like and advanced linguistic features. In this paper, the usage of a commonly used rhetorical device – metaphor – is investigated for social chatbot. Our work first designs a metaphor generation framework, which generates topic-aware and novel figurative sentences. Human annotators validate the novelty and properness of the generated metaphors. More importantly, we evaluate the effects of employing metaphors in human-chatbot conversations. Experiments indicate that our system effectively arouses user interests in communicating with our chatbot, resulting in significantly longer human-chatbot conversations.

10 citations

Book ChapterDOI
Ree C. Ho1
01 Jan 2021
TL;DR: In this article, the authors explored the relationship among trust, commitment, service quality, and technology towards the use of chatbots, and presented an integrative framework for predicting customer bonding with firms.
Abstract: Chatbot has become popular in recent years due to the advancements in artificial intelligence and other underlying technologies. Likewise, increased internet interactivity and smarter mobile devices have specifically attracted more consumers to pursue superior and personalized customer service. The aim of this chapter was therefore to better understand the use of chatbots by online businesses to shed light on its effect on customer service satisfaction. The commitment trust theory served as the underlying theoretical foundation for the conceptual framework of this study. It explored the relationships among trust, commitment, service quality, and technology towards the use of chatbots. Subsequently, customer engagement gained has influenced the knowledge sharing and the referral to other customers. This chapter presented an integrative framework for predicting the use of chatbots to enhance customer bonding with firms. The main contribution was the list of antecedents needed to improve customer engagement in the implementation of chatbots.

10 citations

Proceedings ArticleDOI
18 Dec 2020
TL;DR: This study builds a chatbot system in a closed domain with the RASA framework, using several models such as SVM for classifying intents, CRF for extracting entities and LSTM for predicting action, and uses the kNN algorithm to improve responses from the bot.
Abstract: In this study, we build a chatbot system in a closed domain with the RASA framework, using several models such as SVM for classifying intents, CRF for extracting entities and LSTM for predicting action To improve responses from the bot, the kNN algorithm is used to transform false entities extracted into true entities The knowledge domain of our chatbot is about the College of Information and Communication Technology of Can Tho University, Vietnam We manually construct a chatbot corpus with 19 intents, 441 sentence patterns of intents, 253 entities and 133 stories Experiment results show that the bot responds well to relevant questions

10 citations

Journal ArticleDOI
TL;DR: A developer's perspective is taken, a set of architectural patterns that capture different chatbot integration scenarios are identified, and state-of-the-art development aids are reviewed.
Abstract: Chatbots are software agents that are able to interact with humans in natural language. Their intuitive interaction paradigm is expected to significantly reshape the software landscape of tomorrow, while already today chatbots are invading a multitude of scenarios and contexts. This article takes a developer's perspective, identifies a set of architectural patterns that capture different chatbot integration scenarios, and reviews state-of-the-art development aids.

10 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023916
20221,413
2021564
2020617
2019528
2018326