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Anti-Drug Buddy: A Chatbot for Advocating the Awareness of Anti-Drug

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Advances in Artificial Intelligence (JSAI 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1423))

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Abstract

In recent years, the spread and abuse of drugs have always been the focus of social attention because it can cause serious problems in our society. Although there have been many efforts in the domain of anti-drug, the number of guilty persons in drug cases is still rising. Therefore, there is a need to find a way to promote anti-drug awareness. At present, chatbots on the market cannot be completely applied to the anti-drug domain, so we design a dialog architecture to improve the anti-drug-related QA and to answer more detailed knowledge about drugs. Then, we propose Anti-Drug Buddy that utilizes a chatbot to advocate general knowledge about drugs and deepen anti-drug awareness for people. Currently, our Anti-Drug Buddy is already serving on LINE. For an anti-drug domain, our chatbot can answer users: Information about drugs, How to deal with drug problems, Pushing the latest news and information about drugs.

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References

  1. United Nations Office on Drugs and Crime: 2013 world drug report notes stability in use of traditional drugs and points to alarming rise in new psychoactive substances (2013). https://www.unodc.org/islamicrepublicofiran/en/2013-world-drug-report-notes-stability-in-use-of-traditional-drugs-and-points-to-alarming-rise-in-new-psychoactive-substances.html

  2. Wakefield, M.A., Loken, B., Hornik, R.C.: Use of mass media campaigns to change health behaviour. Lancet 376(9748), 1261–1271 (2010)

    Article  Google Scholar 

  3. Huang, C.M., Wang, J.H., Chen, S.Y.: An investigation of a game-based anti-drug system: addictive learners vs. non-addictive learners. In: 2015 IEEE 15th International Conference on Advanced Learning Technologies, pp. 155–157. IEEE (2015)

    Google Scholar 

  4. Yang, T.C., Chen, M.C., Sun, Y.S.: An investigation of the influence of drug addiction on learning behaviors in a game-based learning environment. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT), pp. 158–162. IEEE (2017)

    Google Scholar 

  5. Ministry of Justice: The statistics on the number of drug offenders (2018). https://anti-drug.moj.gov.tw/Public/Charts/23/21a82272e583784130b63054594c5b0203.html

  6. Shum, H.-Y., He, X., Li, D.: From eliza to xiaoice: challenges and opportunities with social chatbots, arXiv preprint arXiv:1801.01957 (2018)

  7. Leviathan, Y., Matias, Y.: Engineering, Google, Google duplex: An ai system for accomplishing real-world tasks over the phone (2018). https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html

  8. Mnasri, M.: Recent advances in conversational nlp: Towards the standardization of chatbot building, arXiv preprint arXiv:1903.09025 (2019)

  9. Gao, J., Galley, M., Li, L.: Neural approaches to conversational AI. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1371–1374 (2018)

    Google Scholar 

  10. Harms, J.-G., Kucherbaev, P., Bozzon, A., Houben, G.-J.: Approaches for dialog management in conversational agents. IEEE Internet Comput. 23(2), 13–22 (2018)

    Article  Google Scholar 

  11. Hussain, S., Athula, G.: Extending a conventional chatbot knowledge base to external knowledge source and introducing user based sessions for diabetes education. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 698–703. IEEE (2018)

    Google Scholar 

  12. Athreya, R.G., Ngonga Ngomo, A.C., Usbeck, R.: Enhancing community interactions with data-driven chatbots-the dbpedia chatbot. In: Companion Proceedings of the The Web Conference 2018, pp. 143–146 (2018)

    Google Scholar 

  13. Kephart, J.O., Dibia, V.C., Ellis, J., Srivastava, B., Talamadupula, K., Dholakia, M.: An embodied cognitive assistant for visualizing and analyzing exoplanet data. IEEE Internet Comput. 23(2), 31–39 (2019)

    Article  Google Scholar 

  14. Cui, W., Xiao, Y., Wang, H., Song, Y., Hwang, S.-W., Wang, W.: Kbqa: learning question answering over qa corpora and knowledge bases, arXiv preprint arXiv:1903.02419 (2019)

  15. Yu, Z., Papangelis, A., Rudnicky, A.: Ticktock: a non-goal-oriented multimodal dialog system with engagement awareness. In: 2015 AAAI Spring Symposium Series (2015)

    Google Scholar 

  16. Kannan, A., et al.: Smart reply: automated response suggestion for email. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 955–964 (2016)

    Google Scholar 

  17. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

    Google Scholar 

  18. Hu, T., et al.: Touch your heart: a tone-aware chatbot for customer care on social media. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2018)

    Google Scholar 

  19. Xu, B., et al.: CN-DBpedia: a never-ending Chinese knowledge extraction system. In: Benferhat, S., Tabia, K., Ali, M. (eds.) IEA/AIE 2017. LNCS (LNAI), vol. 10351, pp. 428–438. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60045-1_44

    Chapter  Google Scholar 

  20. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018)

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Correspondence to Eric Hsiaokuang Wu .

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Chen, LH., Wu, E.H., Yang, CC., Gao, TY., Tang, LH. (2022). Anti-Drug Buddy: A Chatbot for Advocating the Awareness of Anti-Drug. In: Takama, Y., et al. Advances in Artificial Intelligence. JSAI 2021. Advances in Intelligent Systems and Computing, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-96451-1_18

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