Bilingual Mental Health Chatbot Using DeBERTa-Based Intent Classification and Neural Translation

Bilingual Mental Health Chatbot Using DeBERTa-Based Intent Classification and Neural Translation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bilingual Mental Health Chatbot Using DeBERTa-Based Intent Classification and Neural Translation Desaboyina Vamsi, P. Vidyuallatha, Manikanta Srinivasula This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6996065/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The creation of smart and culturally diverse mental health support systems is a burning issue, particularly in the case of low-resource language communities. This paper has proposed a bilingual conversational agent that used the deep learning model and helped two different users with English and Telugu. The drive is to propel the provision of a scalable language intelligent mental health support by means of natural dialogue systems. Our solution consists of combining a transformer-based architecture based on DeBertas, which processes intent recognition, but also layers of Bidirectional Long Short-Term Memory or BiLSTM to recognize it. Likewise, we combine MarianMT-based neural machine translation models to communicate in both directions between a hybrid and web-based translation between English and Telugu. A validation accuracy of 84.58 percent was accrued, and the chatbot also improved greatly after 50 epochs with an improved intent dataset with emotional and support-seeking patterns. The standard metrics (BLEU, CHRF, TER and ROUGE-L) were used to evaluate translation systems, where the English->Telugu model performed with a BLEU of 58.65, CHRF 80.98, TER 27.64, ROUGE-L 0.09 and Telugu->English model with BLEU 51.45, CHRF 71.13, TER 36.05 and ROUGE-L 0.75. Lastly, a real-time GUI containing chatbot and translation engines was connected to a quality check system based on the BLEU quality assessment, allowing to make mental health conversations across languages in their natural form, with a high level of faithfulness. Bilingual Chatbot Deep Learning Intent Recognition Marianmt Mental Health Neural Machine Translation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6996065","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479461973,"identity":"359ed796-d240-4f15-a41c-3a5f7bc9a9d9","order_by":0,"name":"Desaboyina Vamsi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYLCCBDYgIf+w8cEHIM3GTrQWhuRmwxkgLcxEWQPWkt4mzQOiCWkxOH46+cODMht584aDbdI2v7bJ8zEzMH74mINHy5ncbRIJ59IM5xxsbLbO7btt2MbMwCw5cxtuLZINudsYEtsOM85gZmy8ndtzmxGohY2ZF5+W/rebPyS2/befwcbYIG3Zc9ueoBZ+idwNEoltBxJn8DA2STP8uJ1IhJa3IL8kJ8+QYGw27G24ndzGzNiM1y9s/LmbP/4os7OdIcH+8MGPP7dt57c3H/zwEY8WVMDYBiYbiFUPAn9IUTwKRsEoGAUjBQAA3axR8oy5teoAAAAASUVORK5CYII=","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":true,"prefix":"","firstName":"Desaboyina","middleName":"","lastName":"Vamsi","suffix":""},{"id":479461976,"identity":"4b500e2b-199a-4db7-adba-f6b6b824db64","order_by":1,"name":"P. 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This paper has proposed a bilingual conversational agent that used the deep learning model and helped two different users with English and Telugu. The drive is to propel the provision of a scalable language intelligent mental health support by means of natural dialogue systems. Our solution consists of combining a transformer-based architecture based on DeBertas, which processes intent recognition, but also layers of Bidirectional Long Short-Term Memory or BiLSTM to recognize it. Likewise, we combine MarianMT-based neural machine translation models to communicate in both directions between a hybrid and web-based translation between English and Telugu. A validation accuracy of 84.58 percent was accrued, and the chatbot also improved greatly after 50 epochs with an improved intent dataset with emotional and support-seeking patterns. The standard metrics (BLEU, CHRF, TER and ROUGE-L) were used to evaluate translation systems, where the English-\u0026gt;Telugu model performed with a BLEU of 58.65, CHRF 80.98, TER 27.64, ROUGE-L 0.09 and Telugu-\u0026gt;English model with BLEU 51.45, CHRF 71.13, TER 36.05 and ROUGE-L 0.75. 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