Standard

The Description of The Autism Spectrum Disorder Question Answering Dataset. / Фирсанова, Виктория Игоревна.

Материалы студенческой сессии международной конференции Диалог 2021. http://www.dialog-21.ru/, 2021.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Фирсанова, ВИ 2021, The Description of The Autism Spectrum Disorder Question Answering Dataset. in Материалы студенческой сессии международной конференции Диалог 2021. http://www.dialog-21.ru/, Диалог 2021, Russian Federation, 16/06/21. <http://www.dialog-21.ru/media/5346/firsanova.pdf>

APA

Фирсанова, В. И. (2021). The Description of The Autism Spectrum Disorder Question Answering Dataset. In Материалы студенческой сессии международной конференции Диалог 2021 http://www.dialog-21.ru/media/5346/firsanova.pdf

Vancouver

Фирсанова ВИ. The Description of The Autism Spectrum Disorder Question Answering Dataset. In Материалы студенческой сессии международной конференции Диалог 2021. http://www.dialog-21.ru/. 2021

Author

Фирсанова, Виктория Игоревна. / The Description of The Autism Spectrum Disorder Question Answering Dataset. Материалы студенческой сессии международной конференции Диалог 2021. http://www.dialog-21.ru/, 2021.

BibTeX

@inproceedings{dcf48be08bd34fa4a489850c63d68a50,
title = "The Description of The Autism Spectrum Disorder Question Answering Dataset",
abstract = "The study presents the Autism Spectrum Disorder Question Answering Dataset (ASD QA), a new Russian dataset based on the structure of the Stanford Question Answering Dataset (SQuAD), a machine reading comprehension dataset. The ASD QA dataset is a work in progress. The dataset version described in the paper consists of 1,134 question-answer pairs compiled by the author of the paper from the information website for individuals with autism spectrum disorders (ASD) and Asperger{\textquoteright}s syndrome and their parents. The paper also describes several question-answering models built to analyze the dataset.",
keywords = "Dataset, Question Answering, Dialogue Systems, Machine Reading Comprehension",
author = "Фирсанова, {Виктория Игоревна}",
note = "1. Matroid. (2019). Ilya Sutskever - GPT-2. YouTube. https://www.youtube.com/watch?v=T0I88NhR_9M&amp;ab_channel=Matroid. 2. Kearsley, G. P. (1976). Questions and question asking in verbal discourse: A cross-disciplinary review. Journal of Psycholinguistic Research, 5(4), 355–375. https://doi.org/10.1007/bf01079934 3. Canonico, M., Russis, L.D. (2018) A Comparison and Critique of Natural Language Understanding Tools. CLOUD COMPUTING 2018: The Ninth International Conference on Cloud Computing, GRIDs, and Virtualization, 110–115. 4. Jurafsky, D., & Martin, J. H. (2014). Speech and language processing. Prentice Hall, Pearson Education International. 5. Dhingra, B., Rivard, K., & Cohen, W. (2017). Quasar: Datasets for Question Answering by Search and Reading. ArXiv Preprint ArXiv:1707.03904. 6. Longpre, S., Lu, Y., & Daiber, J. (2020). MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering. ArXiv Preprint ArXiv:2007.15207. 7. Usbeck, R., Gusmita, R. H., Ngomo, A.-C. N., & Saleem, M. (2018). 9th Challenge on Question Answering over Linked Data (QALD-9). Joint Proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4: Natural Language Interfaces for the Web of Data (NLIWOD-4) and 9th Question Answering over Linked Data Challenge (QALD-9) Co-Located with 17th International Semantic Web Conference (ISWC 2018), 58–64. 8. Korablinov, V., & Braslavski, P. (2020). RuBQ: A Russian Dataset for Question Answering over Wikidata. Lecture Notes in Computer Science, 97–110. https://doi.org/10.1007/978-3-030-62466-8_7 9. Rajpurkar, P., Jia, R., & Liang, P. (2018). Know What You Don{\textquoteright}t Know: Unanswerable Questions for SQuAD. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). https://doi.org/10.18653/v1/p18-2124 10. Efimov, P., Chertok, A., Boytsov, L., & Braslavski, P. (2020). SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis. Lecture Notes in Computer Science, 3–15. https://doi.org/10.1007/978-3-030-58219-7_1 11. Firsanova, V. (2021). The Evolution of Chatbots: Psychoanalytics, Business Partners, Soul Mates (Jevoljucija chat-botov: Psihoanalitiki, biznes-partnery, sputniki zhizni). https://github.com/vifirsanova/NLP-Discussion-Group/blob/master/NLP_slides/Chatbots.pdf. 12. Beautiful Soup Documentation. Beautiful Soup Documentation - Beautiful Soup 4.9.0 documentation. (n.d.). https://www.crummy.com/software/BeautifulSoup/bs4/doc/. 13. Python. Python.org. (n.d.). https://www.python.org/. 14. Scikit-learn. scikit-learn: machine learning in Python. (n.d.). https://scikit-learn.org/stable/ . 15. Gage, P. (1994). A New Algorithm for Data Compression. The C User Journal. https://doi.org/10.5555/177910.177914 16. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI. 17. Scikit-learn. scikit-learn: machine learning in Python. (n.d.). https://scikit-learn.org/stable/ . 18. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the Orth American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. https://doi.org/10.18653/v1/n19-1423 19. Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzm{\'a}n, F., … Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.747 20. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS 2019. 21. Geotrend. Geotrend - Revealing the world's connections. (n.d.). https://www.geotrend.fr/en/ .; null ; Conference date: 16-06-2021 Through 19-06-2021",
year = "2021",
month = jun,
language = "English",
booktitle = "Материалы студенческой сессии международной конференции Диалог 2021",
url = "http://www.dialog-21.ru/",

}

RIS

TY - GEN

T1 - The Description of The Autism Spectrum Disorder Question Answering Dataset

AU - Фирсанова, Виктория Игоревна

N1 - 1. Matroid. (2019). Ilya Sutskever - GPT-2. YouTube. https://www.youtube.com/watch?v=T0I88NhR_9M&amp;ab_channel=Matroid. 2. Kearsley, G. P. (1976). Questions and question asking in verbal discourse: A cross-disciplinary review. Journal of Psycholinguistic Research, 5(4), 355–375. https://doi.org/10.1007/bf01079934 3. Canonico, M., Russis, L.D. (2018) A Comparison and Critique of Natural Language Understanding Tools. CLOUD COMPUTING 2018: The Ninth International Conference on Cloud Computing, GRIDs, and Virtualization, 110–115. 4. Jurafsky, D., & Martin, J. H. (2014). Speech and language processing. Prentice Hall, Pearson Education International. 5. Dhingra, B., Rivard, K., & Cohen, W. (2017). Quasar: Datasets for Question Answering by Search and Reading. ArXiv Preprint ArXiv:1707.03904. 6. Longpre, S., Lu, Y., & Daiber, J. (2020). MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering. ArXiv Preprint ArXiv:2007.15207. 7. Usbeck, R., Gusmita, R. H., Ngomo, A.-C. N., & Saleem, M. (2018). 9th Challenge on Question Answering over Linked Data (QALD-9). Joint Proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4: Natural Language Interfaces for the Web of Data (NLIWOD-4) and 9th Question Answering over Linked Data Challenge (QALD-9) Co-Located with 17th International Semantic Web Conference (ISWC 2018), 58–64. 8. Korablinov, V., & Braslavski, P. (2020). RuBQ: A Russian Dataset for Question Answering over Wikidata. Lecture Notes in Computer Science, 97–110. https://doi.org/10.1007/978-3-030-62466-8_7 9. Rajpurkar, P., Jia, R., & Liang, P. (2018). Know What You Don’t Know: Unanswerable Questions for SQuAD. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). https://doi.org/10.18653/v1/p18-2124 10. Efimov, P., Chertok, A., Boytsov, L., & Braslavski, P. (2020). SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis. Lecture Notes in Computer Science, 3–15. https://doi.org/10.1007/978-3-030-58219-7_1 11. Firsanova, V. (2021). The Evolution of Chatbots: Psychoanalytics, Business Partners, Soul Mates (Jevoljucija chat-botov: Psihoanalitiki, biznes-partnery, sputniki zhizni). https://github.com/vifirsanova/NLP-Discussion-Group/blob/master/NLP_slides/Chatbots.pdf. 12. Beautiful Soup Documentation. Beautiful Soup Documentation - Beautiful Soup 4.9.0 documentation. (n.d.). https://www.crummy.com/software/BeautifulSoup/bs4/doc/. 13. Python. Python.org. (n.d.). https://www.python.org/. 14. Scikit-learn. scikit-learn: machine learning in Python. (n.d.). https://scikit-learn.org/stable/ . 15. Gage, P. (1994). A New Algorithm for Data Compression. The C User Journal. https://doi.org/10.5555/177910.177914 16. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI. 17. Scikit-learn. scikit-learn: machine learning in Python. (n.d.). https://scikit-learn.org/stable/ . 18. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the Orth American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. https://doi.org/10.18653/v1/n19-1423 19. Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., … Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.747 20. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS 2019. 21. Geotrend. Geotrend - Revealing the world's connections. (n.d.). https://www.geotrend.fr/en/ .

PY - 2021/6

Y1 - 2021/6

N2 - The study presents the Autism Spectrum Disorder Question Answering Dataset (ASD QA), a new Russian dataset based on the structure of the Stanford Question Answering Dataset (SQuAD), a machine reading comprehension dataset. The ASD QA dataset is a work in progress. The dataset version described in the paper consists of 1,134 question-answer pairs compiled by the author of the paper from the information website for individuals with autism spectrum disorders (ASD) and Asperger’s syndrome and their parents. The paper also describes several question-answering models built to analyze the dataset.

AB - The study presents the Autism Spectrum Disorder Question Answering Dataset (ASD QA), a new Russian dataset based on the structure of the Stanford Question Answering Dataset (SQuAD), a machine reading comprehension dataset. The ASD QA dataset is a work in progress. The dataset version described in the paper consists of 1,134 question-answer pairs compiled by the author of the paper from the information website for individuals with autism spectrum disorders (ASD) and Asperger’s syndrome and their parents. The paper also describes several question-answering models built to analyze the dataset.

KW - Dataset

KW - Question Answering

KW - Dialogue Systems

KW - Machine Reading Comprehension

UR - http://www.dialog-21.ru/dialogue2021/results/program/day-4/

M3 - Conference contribution

BT - Материалы студенческой сессии международной конференции Диалог 2021

CY - http://www.dialog-21.ru/

Y2 - 16 June 2021 through 19 June 2021

ER -

ID: 84634087