Analytical and Sentiment based text generative chatbot

Sawant, Sahil; Vishwakarma, Ankit; Sawant, Prerana; Bhavathankar, Prasenjit

doi:10.1109/icccnt51525.2021.9580069

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…This involves accurately recognizing emotions from user inputs and providing contextually appropriate responses [25]. Balancing the complexity of emotion prediction with real-time interaction requirements requires careful consideration [20]. Furthermore, seamless integration of emotion prediction into the chatbot's architecture is essential for effective performance [26].Identified objectives and requirements of such chatbot systems are listed below.…”

Section: Methodsmentioning

confidence: 99%

“…The study, conducted in collaboration with i-CATS University College, reveals a limitation in the rule-based system, leading to repetitive responses for the same queries. This suggests a need for further development to enhance the chatbot's diversity in responses [20].…”

Section: Related Workmentioning

confidence: 99%

“…In [26] emotion detection and response generation are not integrated therefore the produced response does not align with the emotion of the user, The proposed system integrated emotion prediction and response generation to produce responses that aligns with the user emotion. Responses may be repetitive [20] when getting the highest value of cosine distance therefore when same text is taken as the input it retrieves the next highest cosine distance value with a assigned threshold.…”

Section: Algorithmmentioning

confidence: 99%

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Retrieval based Emotionally responsive chatbot Utilizing emotions clustering and XAI Techniques.

Ashen

2024

Preprint

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Chatbots are becoming increasingly prevalent in our daily lives, handling a variety of tasks from booking flights to checking the balance of a bank account. However, these interactions are often limited to transactional nature, lacking the depth and nuance of natural human conversations. Therefore, while chatbots are becoming more sophisticated, they still have a significant way to go in terms of authentic human-like interaction. Enhancing chatbots' ability to interact with humans and making them more user-friendly is a crucial challenge in contemporary conversational technology. One promising approach is to imbue these chatbots with the ability to express and understand emotions. Hence, the focus is on developing empathetic dialogue systems that effectively model empathy, ultimately making chatbots more human-like in their interactions with users. In this project, A retrieval-based approach is adopted to mitigate the limitations encountered in generative-based models, particularly concerning logical coherence and response appropriateness. Unlike generative models, retrieval-based approaches offer a more pragmatic solution that demands fewer computational resources. Through the integration of deep learning techniques, transparency in decision-making processes is ensured by eXplainable Artificial Intelligence (XAI), providing users with insights into the chatbot's reasoning. Furthermore, clustering techniques are leveraged to generalize various emotion types, allowing the chatbot to cater to a broader spectrum of emotional inputs effectively.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

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Retrieval based Emotionally responsive chatbot Utilizing emotions clustering and XAI Techniques.

Ashen

2024

Preprint

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“…Recentemente, modelos generativos baseados em redes neurais profundas têm se tornado o estado da arte no desenvolvimento de chatbots, pois não se limitam a respostas prédefinidas. Tais modelos geram novas respostas a partir de uma base de dados de treinamento de conversac ¸ão utilizando uma variedade de abordagens da área de aprendizado profundo, do inglês Deep Learning [Sawant et al 2021].…”

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Chatbots Generativos como Ferramentas de Apoio ao Ensino em Cursos na Área de Ferrovias

Lopes¹,

Oliveira²,

Gazolli³

2022

Anais Do XIX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2022)

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Chatbots são sistemas de conversação capazes de simular interações utilizando linguagem natural. Essa tecnologia permite a interação com os usuários de forma rápida, e seu uso como ferramenta de apoio ao ensino oferece aos estudantes uma nova forma de acesso ao conteúdo. Este trabalho apresenta uma base de dados de domínio específico, bem como sua utilização na construção de um chatbot generativo para auxiliar alunos na área de Ferrovias. Para isso, foram utilizadas as redes neurais BiLSTM e GRU, ambas em uma arquitetura do tipo codificador-decodificador com mecanismo de atenção. Os experimentos realizados demonstraram que a arquitetura usando GRUs obteve melhor desempenho com base nas medidas de avaliação do BLEU e ROUGE-L.

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation

Oviya,

Raja

2024

2024 14th International Conference on Cloud Computing, Data Science &Amp;amp; Engineering (Confluence)

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Analytical and Sentiment based text generative chatbot

Cited by 5 publications

References 10 publications

Retrieval based Emotionally responsive chatbot Utilizing emotions clustering and XAI Techniques.

Retrieval based Emotionally responsive chatbot Utilizing emotions clustering and XAI Techniques.

Chatbots Generativos como Ferramentas de Apoio ao Ensino em Cursos na Área de Ferrovias

EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation

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