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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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Proceedings ArticleDOI
05 Nov 2020
TL;DR: In this article, the authors explored the ways of communication through neural network chatbot by using the Sequence to Sequence model with Attention Mechanism based on RNN encoder decoder model.
Abstract: Educational chatbots have great potential to help students, teachers and education staff. They provide useful information in educational sectors for inquirers. Neural chatbots are more scalable and popular than earlier ruled-based chatbots. Recurrent Neural Network based Sequence to Sequence (Seq2Seq) model can be used to create chatbots. Seq2Seq is adapted for good conversational model for sequences especially in question answering systems. In this paper, we explore the ways of communication through neural network chatbot by using the Sequence to Sequence model with Attention Mechanism based on RNN encoder decoder model. This chatbot is intended to be used in university education sector for frequently asked questions about the university and its related information. It is the first Myanmar Language University Chatbot using neural network model and gets 0.41 BLEU score.

9 citations

Book ChapterDOI
01 May 2021
TL;DR: In this paper, a self-harm classifier was designed to predict whether a user's response to a chatbot indicates intent for selfharm, based on text input from the user.
Abstract: Chatbots potentially address deficits in availability of the traditional health workforce and could help to stem concerning rates of youth mental health issues including high suicide rates. While chatbots have shown some positive results in helping people cope with mental health issues, there are yet deep concerns regarding such chatbots in terms of their ability to identify emergency situations and act accordingly. Risk of suicide/self-harm is one such concern which we have addressed in this project. A chatbot decides its response based on the text input from the user and must correctly recognize the significance of a given input. We have designed a self-harm classifier which could use the user's response to the chatbot and predict whether the response indicates intent for self-harm. With the difficulty to access confidential counselling data, we looked for alternate data sources and found Twitter and Reddit to provide data similar to what we would expect to get from a chatbot user. We trained a sentiment analysis classifier on Twitter data and a self-harm classifier on the Reddit data. We combined the results of the two models to improve the model performance. We got the best results from a LSTM-RNN classifier using BERT encoding. The best model accuracy achieved was 92.13%. We tested the model on new data from Reddit and got an impressive result with an accuracy of 97%. Such a model is promising for future embedding in mental health chatbots to improve their safety through accurate detection of self-harm talk by users.

9 citations

Patent
30 Oct 2017
TL;DR: In this paper, a method for generating a chatbot interface for an application programming interface (API) that interacts with networked applications is presented, which can be used to generate a conversation specification in the chatbot data structure.
Abstract: Method and system are provided for generating a chatbot interface for an application programming interface (API) that interacts with networked applications. The method may include: receiving as an input a definition document for an API that interacts with networked applications and parsing the definition document to identify intents and entities and obtain examples of the identified intents and entities. The method may convert the definition document to a chatbot data structure including: extracting the intents and entities and their relationship to objects and fields in the API from the definition document; and training the chatbot data structure with the example intents and entities to generate a conversation specification in the chatbot data structure. The method may then generate a chatbot interface for the API.

9 citations

Journal ArticleDOI
TL;DR: The experience resulting from using a chatbot to support learning in accounting students for the teaching of tax regulations related to the Chilean tax system is shown, comparing two types of tools, on the one hand, a free conversation chatbot using natural language processing versus a rule-based chatbot driven by a decision tree.
Abstract: Article history: Received: 30 August, 2020 Accepted: 01 November, 2020 Online: 20 November, 2020 Teaching tax-related regulations have always been a challenge due to the inclusion of external variables that hinder the learning process, such as the high complexity of tax systems and legislation variability. Universities have sought different alternatives to support the teaching process both outside and inside the classroom, ranging from recreational activities to active learning. This article will show the experience resulting from using a chatbot to support learning in accounting students for the teaching of tax regulations related to the Chilean tax system, comparing two types of tools, on the one hand, a free conversation chatbot using natural language processing versus a rule-based chatbot driven by a decision tree. The experimentation process was carried out with 50 higher education students, divided into an experimental group and a control group, in two different courses. The results obtained demonstrated in both cases greater effectiveness of the use of the chatbot in learning the tax matter, both in the free conversation chatbot where the experimental group obtained a 15.7% improvement versus the control group that obtained a 1.05% improvement, as in the chatbot that applied decision tree where the experimental group obtained a 32% improvement versus the control group with 5.2%. Considering the complexity of the content in tax matters and the little innovation in the existing teaching subjects in the area and that the students improve learning using both chatbot tools compared to other learning techniques, is considered a relevant contribution to teaching innovation.

9 citations


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