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2021 | OriginalPaper | Buchkapitel

Mental Healthcare Chatbot Using Sequence-to-Sequence Learning and BiLSTM

verfasst von : Afsana Binte Rakib, Esika Arifin Rumky, Ananna J. Ashraf, Md. Monsur Hillas, Muhammad Arifur Rahman

Erschienen in: Brain Informatics

Verlag: Springer International Publishing

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Abstract

Mental health is an important aspect of an individual’s well-being which still continues to remain unaddressed. With the rise of the COVID-19 pandemic, mental health has far continued to decline, especially amongst the younger generation. The aim of this research is to raise awareness about mental health while simultaneously working towards removing the societal stigma surrounding it. Thus, in this paper, we have created an integrated chatbot that is specifically geared towards mentally ill individuals. The chatbot responds empathetically which is built using a Sequence-to-Sequence (Seq2Seq) encoder-decoder architecture. The encoder uses Bi-directional Long Short Term Memory (BiLSTM). To compare the performance, we used Beam Search and Greedy Search. We found Beam Search decoder performs much better, providing empathetic responses to the user with greater precision in terms of BLEU score.

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Vorheriges Kapitel D3mciAD: Data-Driven Diagnosis of Mild Cognitive Impairment Utilizing Syntactic Images Generation and Neural Nets

Nächstes Kapitel A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis

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Titel: Mental Healthcare Chatbot Using Sequence-to-Sequence Learning and BiLSTM
verfasst von: Afsana Binte Rakib
Esika Arifin Rumky
Ananna J. Ashraf
Md. Monsur Hillas
Muhammad Arifur Rahman
Verlag: Springer International Publishing
Buch: Brain Informatics
Print ISBN: 978-3-030-86992-2

Electronic ISBN: 978-3-030-86993-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-86993-9_34

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