Mapeamento do Perfil das Mulheres Brasileiras em Processamento de Linguagem Natural
Abstract
Knowing the profile of Brazilian women who work in Natural Language Processing (NLP) is an important step towards the development of policies aimed at increasing inclusion and diversity in this field. This is the first study conducted in Brazil for this purpose. Based on data from public survey, Lattes and Linkedin, we found that the profile is that of a background in computer science or linguistics, working in companies or universities, but with little ethnic diversity and apparent difficulty in balancing professional life and motherhood. Analyzing more specifically the group “Brasileiras em PLN” (Brazilian Women in NLP), we found that we have a significant capacity for publication and supervision, but still a low level of collaboration among our members.
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Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023). A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.
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Dong, C., Li, Y., Gong, H., Chen, M., Li, J., Shen, Y., and Yang, M. (2022). A survey of natural language generation. ACM Computing Surveys, 55(8):1–38.
Freitas, C. (2022). Linguística Computacional. Parábola, São Paulo.
Hango, D. (2013). Gender Differences in Science, Technology, Engineering, Mathematics and Computer Science (STEM) Programs at University. Insights on Canadian Society.
Hill, C., Corbett, C., and St. Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. American Association of University Women., Washington, D.C.
Klocker, N. and Drozdzewski, D. (2012). Commentary: Career progress relative to opportunity: how many papers is a baby ’worth’?
Lorens, A. L., Botelho, J., Moura, A. F., Duarte, B., and Moro, M. (2020). Participação feminina em comitês de programa de simpósios da computação. In Anais do XIV Women in Information Technology, pages 90–99, Porto Alegre, RS, Brasil. SBC.
Metz, I. (2005). Advancing the careers of women with children. Career Development International.
Mohammad, S. M. (2020). Gender gap in natural language processing research: Disparities in authorship and citations. In Proceedings of the 58th Annual Meeting of ACL, pages 7860–7870, Online. Association for Computational Linguistics.
Richter, A., Yamamoto, J., and Frachtenberg, E. (2023). Why are there so few women in computer systems research? Computer, 56(2):101–105.
Wang, M.-T. and Degol, J. L. (2017). Gender gap in science, technology, engineering, and mathematics (stem): Current knowledge, implications for practice, policy, and future directions. Educational psychology review, 29:119–140.
Wang, Y., Wang, Y., Liu, J., and Liu, Z. (2020). A comprehensive survey of grammar error correction. arXiv preprint arXiv:2005.06600.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Zhang, L., Wang, S., and Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1253.
Published
2023-08-06
How to Cite
CASELI, Helena; AMORIM, Evelin; SCHNEIDER, Elisa Terumi Rubel; FREITAS, Leidiana Iza Andrade; RODRIGUES, Jéssica; NUNES, Maria das Graças V..
Mapeamento do Perfil das Mulheres Brasileiras em Processamento de Linguagem Natural. In: WOMEN IN INFORMATION TECHNOLOGY (WIT), 17. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 182-193.
ISSN 2763-8626.
DOI: https://doi.org/10.5753/wit.2023.229504.
