Author: Rakib, A. B.; Rumky, E. A.; Ashraf, A. J.; Hillas, M. M.; Rahman, M. A.
Title: Mental Healthcare Chatbot Using Sequence-to-Sequence Learning and BiLSTM Cord-id: plik0zoe Document date: 2021_1_1
ID: plik0zoe
Snippet: 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 in
Document: 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. © 2021, Springer Nature Switzerland AG.
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