Chatbots Generativos como Ferramentas de Apoio ao Ensino em Cursos na Área de Ferrovias
Resumo
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.
Referências
AbuShawar, B. and Atwell, E. (2015). Alice chatbot: Trials and outputs. Computación y Sistemas, 19(4):625-632.
Adamopoulou, E. and Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2:100006.
Aleedy, M., Shaiba, H., and Bezbradica, M. (2019). Generating and analyzing chatbot responses using natural language processing. International Journal of Advanced Computer Science and Applications, 10(9).
Arora, B., Chaudhary, D. S., Satsangi, M., Yadav, M., Singh, L., and Sudhish, P. S. (2020). Agribot: a natural language generative neural networks engine for agricultural applications. In 2020 International Conference on Contemporary Computing and Applications (IC3A), pages 28-33. IEEE.
Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
Caldarini, G., Jaf, S., and McGarry, K. (2022). A literature survey of recent advances in chatbots. Information, 13(1).
Chang, I.-C., Shih, Y.-S., and Kuo, K.-M. (2022). Why would you use medical chatbots? interview and survey. International Journal of Medical Informatics, 165:104827.
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724-1734, Doha, Qatar. Association for Computational Linguistics.
Dabre, R., Chu, C., and Kunchukuttan, A. (2020). A survey of multilingual neural machine translation. ACM Computing Surveys (CSUR), 53(5):1-38.
Dhyani, M. and Kumar, R. (2021). An intelligent chatbot using deep learning with bidirectional rnn and attention model. Materials today: proceedings, 34:817-824.
Galassi, A., Lippi, M., and Torroni, P. (2020). Attention in natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32(10):4291-4308.
Galvão, A., Barros, F., Neves, A., and Ramalho, G. (2004). Persona-aiml: An architecture developing chatterbots with personality. pages 1266-1267.
Hou, S.-L., Huang, X.-K., Fei, C.-Q., Zhang, S.-H., Li, Y.-Y., Sun, Q.-L., and Wang, C.-Q. (2021). A survey of text summarization approaches based on deep learning. Journal of Computer Science and Technology, 36(3):633-663.
Jerald, J. (2015). The VR book: Human-centered design for virtual reality. Morgan & Claypool.
Kim, J., Oh, S., Kwon, O.-W., and Kim, H. (2019). Multi-turn chatbot based on querycontext attentions and dual wasserstein generative adversarial networks. Applied Sciences, 9(18):3908.
Kuhail, M. A., Alturki, N., Alramlawi, S., and Alhejori, K. (2022). Interacting with educational chatbots: A systematic review. Education and Information Technologies, pages 1-46.
Li, W., Wu, H., Zhu, N., Jiang, Y., Tan, J., and Guo, Y. (2021). Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (gru). Information Processing in Agriculture, 8(1):185-193.
Lin, C.-Y. (2004). Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81.
McTear, M. (2020). Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
Misischia, C. V., Poecze, F., and Strauss, C. (2022). Chatbots in customer service: Their relevance and impact on service quality. Procedia Computer Science, 201:421-428. The 13th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 5th International Conference on Emerging Data and Industry 4.0 (EDI40).
Mondal, A., Dey, M., Das, D., Nagpal, S., and Garda, K. (2018). Chatbot: An automated conversation system for the educational domain. In 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), pages 1-5. IEEE.
Neves, A., Barros, F., and Hodges, C. (2006). iaiml: a mechanism to treat intentionality in aiml chatterbots. In 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), pages 225-231.
Oliveira, H. T. A. d., Gadelha, R. N., de Azevedo, R. R., Júnior, J. B. D., Dias, G. A., and Freitas, F. (2010). Dr. pierre: Um chatterbot com intenção e personalidade baseado em ontologias para apoiar o ensino de psiquiatria. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 1.
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002). Bleu: A method for automatic evaluation of machine translation. ACL '02, page 311-318, USA. Association for Computational Linguistics.
Rana, R. (2016). Gated recurrent unit (gru) for emotion classification from noisy speech. arXiv preprint arXiv:1612.07778.
Sawant, S., Vishwakarma, A., Sawant, P., and Bhavathankar, P. (2021). Analytical and sentiment based text generative chatbot. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pages 1-7.
Sutskever, I., Vinyals, O., and Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems Volume 2, NIPS'14, page 3104-3112, Cambridge, MA, USA. MIT Press.
Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX(236):433-460.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Vieira, M. and Silva, C. (2020). A educação no contexto da pandemia de covid-19: uma revisão sistemática de literatura. Revista Brasileira de Informática na Educação, 28(0):1013-1031.
Weizenbaum, J. (1966). Eliza-a computer program for the study of natural language communication between man and machine. Commun. ACM, 9(1):36-45.
Zalake, N. and Naik, G. (2019). Generative chat bot implementation using deep recurrent neural networks and natural language understanding. In Proceedings 2019: Conference on Technologies for Future Cities (CTFC).
Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., and Dolan, B. (2020). Dialogpt: Large-scale generative pre-training for conversational response generation. In ACL, system demonstration.
Adamopoulou, E. and Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2:100006.
Aleedy, M., Shaiba, H., and Bezbradica, M. (2019). Generating and analyzing chatbot responses using natural language processing. International Journal of Advanced Computer Science and Applications, 10(9).
Arora, B., Chaudhary, D. S., Satsangi, M., Yadav, M., Singh, L., and Sudhish, P. S. (2020). Agribot: a natural language generative neural networks engine for agricultural applications. In 2020 International Conference on Contemporary Computing and Applications (IC3A), pages 28-33. IEEE.
Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
Caldarini, G., Jaf, S., and McGarry, K. (2022). A literature survey of recent advances in chatbots. Information, 13(1).
Chang, I.-C., Shih, Y.-S., and Kuo, K.-M. (2022). Why would you use medical chatbots? interview and survey. International Journal of Medical Informatics, 165:104827.
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724-1734, Doha, Qatar. Association for Computational Linguistics.
Dabre, R., Chu, C., and Kunchukuttan, A. (2020). A survey of multilingual neural machine translation. ACM Computing Surveys (CSUR), 53(5):1-38.
Dhyani, M. and Kumar, R. (2021). An intelligent chatbot using deep learning with bidirectional rnn and attention model. Materials today: proceedings, 34:817-824.
Galassi, A., Lippi, M., and Torroni, P. (2020). Attention in natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32(10):4291-4308.
Galvão, A., Barros, F., Neves, A., and Ramalho, G. (2004). Persona-aiml: An architecture developing chatterbots with personality. pages 1266-1267.
Hou, S.-L., Huang, X.-K., Fei, C.-Q., Zhang, S.-H., Li, Y.-Y., Sun, Q.-L., and Wang, C.-Q. (2021). A survey of text summarization approaches based on deep learning. Journal of Computer Science and Technology, 36(3):633-663.
Jerald, J. (2015). The VR book: Human-centered design for virtual reality. Morgan & Claypool.
Kim, J., Oh, S., Kwon, O.-W., and Kim, H. (2019). Multi-turn chatbot based on querycontext attentions and dual wasserstein generative adversarial networks. Applied Sciences, 9(18):3908.
Kuhail, M. A., Alturki, N., Alramlawi, S., and Alhejori, K. (2022). Interacting with educational chatbots: A systematic review. Education and Information Technologies, pages 1-46.
Li, W., Wu, H., Zhu, N., Jiang, Y., Tan, J., and Guo, Y. (2021). Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (gru). Information Processing in Agriculture, 8(1):185-193.
Lin, C.-Y. (2004). Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81.
McTear, M. (2020). Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
Misischia, C. V., Poecze, F., and Strauss, C. (2022). Chatbots in customer service: Their relevance and impact on service quality. Procedia Computer Science, 201:421-428. The 13th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 5th International Conference on Emerging Data and Industry 4.0 (EDI40).
Mondal, A., Dey, M., Das, D., Nagpal, S., and Garda, K. (2018). Chatbot: An automated conversation system for the educational domain. In 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), pages 1-5. IEEE.
Neves, A., Barros, F., and Hodges, C. (2006). iaiml: a mechanism to treat intentionality in aiml chatterbots. In 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), pages 225-231.
Oliveira, H. T. A. d., Gadelha, R. N., de Azevedo, R. R., Júnior, J. B. D., Dias, G. A., and Freitas, F. (2010). Dr. pierre: Um chatterbot com intenção e personalidade baseado em ontologias para apoiar o ensino de psiquiatria. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 1.
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002). Bleu: A method for automatic evaluation of machine translation. ACL '02, page 311-318, USA. Association for Computational Linguistics.
Rana, R. (2016). Gated recurrent unit (gru) for emotion classification from noisy speech. arXiv preprint arXiv:1612.07778.
Sawant, S., Vishwakarma, A., Sawant, P., and Bhavathankar, P. (2021). Analytical and sentiment based text generative chatbot. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pages 1-7.
Sutskever, I., Vinyals, O., and Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems Volume 2, NIPS'14, page 3104-3112, Cambridge, MA, USA. MIT Press.
Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX(236):433-460.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Vieira, M. and Silva, C. (2020). A educação no contexto da pandemia de covid-19: uma revisão sistemática de literatura. Revista Brasileira de Informática na Educação, 28(0):1013-1031.
Weizenbaum, J. (1966). Eliza-a computer program for the study of natural language communication between man and machine. Commun. ACM, 9(1):36-45.
Zalake, N. and Naik, G. (2019). Generative chat bot implementation using deep recurrent neural networks and natural language understanding. In Proceedings 2019: Conference on Technologies for Future Cities (CTFC).
Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., and Dolan, B. (2020). Dialogpt: Large-scale generative pre-training for conversational response generation. In ACL, system demonstration.
Publicado
28/11/2022
Como Citar
LOPES, Eduardo dos Santos; OLIVEIRA, Hilário Tomaz Alves de; GAZOLLI, Kelly Assis de Souza.
Chatbots Generativos como Ferramentas de Apoio ao Ensino em Cursos na Área de Ferrovias. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 352-363.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2022.227611.
