@conference{ccc20e5e35574679b96674d5d6a241ce,
title = "Two Approaches to Building Dialogue Systems for People on the Spectrum",
abstract = "The paper presents a study on combining model- and data-centric approaches tobuilding a question answering system for inclusion of people with autism spectrumdisorder. The study shows that applying sequentially model- and data-centricapproaches might allow achieving higher metric scores on closed-domain lowresourced datasets.",
author = "Фирсанова, {Виктория Игоревна}",
note = "[1] Wang, L.L., Lo, K., Chandrasekhar, Y., Reas, R., Yang, J., Burdick, D., Eide, D., Funk, K., Katsis, Y., Kinney, R., Li, Y., Liu, Z., Merrill, W., Mooney, P., Murdick, D., Rishi, D., Sheehan, J., Shen, Z., Stilson, B., Wade, A.D., Wang, K., Wang, N.X.R., Wilhelm, C., Xie, B., Raymond, D., Weld, D.S., Etzioni, O. & Kohlmeier, S. (2020) CORD-19: The COVID-19 Open Research Dataset. ACL NLP-COVID Workshop 2020. [2] Chakravarthi, B.R. (2020) HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion. In: Proceedings of the Third Workshop on Computational Modeling of PEople{\textquoteright}s Opinions, PersonaLity, and Emotions in Social media, pp. 41–53. Barcelona, Spain (Online). [3] Mamas, C. (2019) Learn to Conduct Descriptive Whole Social Network Analysis Within an Educational Setting in Ucinet With Data From the Inclusive Education Project (2015–2018) SAGE Research Methods Datasets Part 2. [4] Firsanova, V. (2021) Autism Spectrum Disorder and Asperger Syndrome Question Answering Dataset 1.0 https://figshare.com/articles/dataset/Autism_Spectrum_Disorder_and_Asperger_ Syndrome_Question_Answering_Dataset_1_0/13295831. Last accessed 25 Jul 2021. [5] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N. Kaiser, D., Polosukhin, I. (2017) Attention is All You Need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS{\textquoteright}17), pp. 6000–6010. New York: Curran Associates Inc. [6] Rajpurkar, P., Jia, R., Liang, P. (2018) Know What You Don{\textquoteright}t Know: Unanswerable Questions for SQuAD. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 784–789. Melbourne: Association for Computational Linguistics. [7] Zhang, Z., Zhao, H., Wang, R. (2020) Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond. Computational Linguistics Volume 1, Number 1, pp. 1–51. Association for Computational Linguistics. [8] Autistic City. https://aspergers.ru/. Last accessed 30 Sep 2021. [9] Google Colab. https://colab.research.google.com/. Last accessed 30 Sep 2021. [10] Devlin, J., Chang, M-W., Lee, K., Toutanova, K. (2019) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Minneapolis, Minnesota: Association for Computational Linguistics. [11] Sanh, V., Debut, L., Chaumond, J., Wolf, T. (2019) DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. In: 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS, pp. 1–5. [12] Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzm{\'a}n, F., Grave, E., Ott, M., Zettlemoyer, L., Stoyanov, V. (2020) Unsupervised Cross-lingual Representation Learning at Scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440–8451. Association for Computational Linguistics. [13] Geotrend - One click for intelligent data, https://www.geotrend.fr/. Last accessed 30 Sep 2021. [14] Official evaluation script for SQuAD version 2.0., https://worksheets.codalab.org/rest/ bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/. Last accessed 30 Sep 2021.{\textquoteright} [15] A Chat with Andrew on MLOps: From Model-centric to Data-centric AI, https://www.youtube.com/ watch?v=06-AZXmwHjo&ab_channel=DeepLearningAI. Last accessed 22 Nov 2021.; Conference on Neural Information Processing Systems, NeurIPS ; Conference date: 06-12-2021 Through 14-12-2021",
year = "2021",
month = dec,
language = "English",
url = "https://nips.cc/",
}
