Towards Robust, Trustworthy Natural Language Understanding (and a Ph.D. degree)

◾Towards Robust, Trustworthy Natural Language Understanding (and a Ph.D. degree)

◾ Speaker:
Abhilasha Ravichander

◾ Talk Description:
This talk is part of the #women_in_nlp talk series, inviting women who successfully carved their career path in NLP to share their experiences and advice. Everyone is welcome to attend the talk not only women.

◾ Abstract:
This talk will have two parts. In the first part, I will describe my research in evaluating and analyzing natural language understanding (NLU) systems. Neural models of language understanding have established state-of-the-art performance on several NLU benchmarks. However, little is known about their operational boundaries: when and where they are likely to fail, and the kinds of mechanisms that they use to perform tasks. I will present an overview of my research in this area, and discuss challenges that still remain to build more trustworthy NLU systems. In the second part of this talk, I will describe navigating the day-to-day of being a Ph.D. student in NLP, and my strategies for creating a joyful research life. The talk will conclude with open-ended QA, where I will answer literally any question.

◾ Bio:
Abhilasha Ravichander is a Ph.D. student at Carnegie Mellon University, working in the Language Technologies Institute. Her research focuses on understanding the neural model performance and consequently developing more robust and trustworthy NLP technologies. Her work received the "Area Chair Favorite Paper" award at COLING 2018, and she was selected as a “Rising Star in Data Science” by the University of Chicago Rising Stars workshop committee. She has also been the recipient of the outstanding reviewer awards at ACL and EMNLP, serves as co-chair of the socio-cultural inclusion committee for NAACL 2022, and co-organizes the ‘NLP WIth Friends’ seminar series. In the past, she interned at Allen Institute for AI and Microsoft Research, where she worked on understanding how deep learning models process challenging semantic phenomena in natural language. Abhilasha's website is http://cs.cmu.edu/~aravicha

◾ About #women_in_nlp
Website: https://efatmae.github.io/womenInNlp/
Twitter: https://twitter.com/fatmaElsafoury
Slack channel on dair.ai: https://dairai.slack.com/archives/C01J0GXJMD1

◾ About dair.ai
Website: https://dair.ai/
GitHub: https://github.com/dair-ai
Twitter: https://twitter.com/dair_ai
Newsletter: https://dair.ai/newsletter/
Slack: https://join.slack.com/t/dairai/shared_invite/zt-pcxkmoip-b4nJkci8L_dynpMwLvlCcQ

◾ Code of Conduct: https://github.com/dair-ai/dair-ai.github.io/blob/master/CODE_OF_CONDUCT.md