The Design and Implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning

Shi, Nuobei; Zeng, Qin; Lee, Raymond

doi:10.5121/csit.2020.101124

Cited by 11 publications

(3 citation statements)

References 5 publications

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“…Researchers have also demonstrated how unsupervised pre-training of huge language models on a vast corpus of data leads to improved performance when fine-tuned on specific tasks. This can be observed clearly when we look at OpenAI's GPT series: GPT, GPT-2, and GPT-3 which is the best language model the world has seen yet, with its ability to cater to any language task, be it question-answering, reading comprehension, text summarization, text generation or conversation modeling [12], [13].…”

Section: Background Of Conversational Artificial Intelligencementioning

confidence: 96%

TherapyBot: a chatbot for mental well-being using transformers

Dharrao,

Gite

2024

IJAAS

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The field of natural language processing (NLP) and conversational artificial intelligence (AI) has one ingenious application in the psychological space. Depression and anxiety are two major issues that the world is facing, with close to 41% of adults reporting these symptoms in the United States alone, as of December 2020. It has also been observed that most of the people are not open about it. As a result, it is critical to address this issue on a global scale. Developed countries reportedly have 9 psychiatrists per 100,000 people. One way to mitigate this is the use of chatbots. We propose a transformer-based methodology to build a therapy bot that has been trained on a combination of open-domain conversations from a publicly available dataset and therapist-client conversations from a self-constructed dataset. This end-to-end data-driven model shows quality performance in conversations and adds value by aiding in the case of mental health issues. The proposed architecture is proven to be effective in its usability in the psychological space for both single-turn and multi-turn dialogue. The performance of the proposed system shows loss is 0.29 and perplexity is 1.34, both metrics keeps gradually decreasing and it means an improvement in performance of chatbots system.

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Section: Background Of Conversational Artificial Intelligencementioning

confidence: 96%

TherapyBot: a chatbot for mental well-being using transformers

Dharrao,

Gite

2024

IJAAS

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“…, for instance, initialize parts of their generative system with a pretrained BERT model, and Gu et al (2020) finetune BERT for multi-turn response selection in retrievalbased chatbots. Shi et al (2020) introduce an English language-learning chatbot based on GPT-2. Boyd et al (2020) condition a GPT-2 model for dialogue generation on several previous conversations of a single individual to get it to use that individual's style.…”

Section: Training and Data Augmentationmentioning

confidence: 99%

Open-domain Dialogue Generation: What We Can Do, Cannot Do, And Should Do Next

Kann¹,

Ebrahimi²,

Koh³

et al. 2022

Proceedings of the 4th Workshop on NLP for Conversational AI

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Human-computer conversation has long been an interest of artificial intelligence and natural language processing research. Recent years have seen a dramatic improvement in quality for both task-oriented and open-domain dialogue systems, and an increasing amount of research in the area. The goal of this work is threefold: (1) to provide an overview of recent advances in the field of open-domain dialogue, (2) to summarize issues related to ethics, bias, and fairness that the field has identified as well as typical errors of dialogue systems, and (3) to outline important future challenges. We hope that this work will be of interest to both new and experienced researchers in the area. Open-domain Dialogue GenerationWe use the following definition for open-domain dialogue generation, the task of a social chatbot or socialbot: Given zero or more previous dialogue

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“…Figure 13: System Flow of chatbot. [13] With the extension of system flow, in our architecture, the application layer are all English learning levels, which detail functions shown at implementation parts with mini-program User Interface and Develop Tool.…”

Section: Chatbot System Flowmentioning

confidence: 99%

The Design and Implementation of Intelligent English Learning Chabot based on Transfer Learning Technology

Shi¹,

Zeng²,

Lee³

2021

Adv. sci. technol. eng. syst. j.

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Chatbot operates task-oriented customer services in special and open domains at different mobile devices. Its related products such as knowledge base Question-Answer System also benefit daily activities. Chatbot functions generally include automatic speech recognition (ASR), natural language understanding (NLU), dialogue management (DM), natural language generation (NLG) and speech synthesis (SS). In this paper, we proposed a Transfer-based English Language learning chatbot with three learning system levels for real-world application, which integrate recognition service from Google and GPT-2 Open AI with dialogue tasks in NLU and NLG at a WeChat mini-program. From operational perspective, three levels for learning languages systematically were devised: phonetics, semantic and "free-style conversation" simulation in English. First level is to correct pronunciation in voice recognition and learning sentence syntactic. Second is a converse special-domain and the highest third level is a language chatbot communication as freestyle conversation agent. From implementation perspective, the Language Learning agent integrates into a WeChat mini-program to devise three user interface levels and to finetune transfer learning as back-end language model to generate responses for users. With the combination of the two parts about operation and implementation, based on the Neural Network model of transfer learning technology, different users test the system with open-domain topic acquiring good communication experience and proved it ready to be the industrial application to be used.

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The Design and Implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning

Cited by 11 publications

References 5 publications

TherapyBot: a chatbot for mental well-being using transformers

TherapyBot: a chatbot for mental well-being using transformers

Open-domain Dialogue Generation: What We Can Do, Cannot Do, And Should Do Next

The Design and Implementation of Intelligent English Learning Chabot based on Transfer Learning Technology

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