Lei Sha
Lei Sha
Interests
Controllable information extraction, Controllable text generation, Natural language understanding
Biography
I am a Postdoctoral researcher advised by Prof. Thomas Lukasiewicz in the Intelligent System Lab, University of Oxford. Previously, I was an NLP research scientist in Apple. While at Apple, I am responsible for Siri’s module, such as domain classification and chit-chat dialogue. Before that, in 2018, I obtained my PhD degree and graduated from Peking University, China, where I worked with Prof. Zhifang Sui, Prof. Baobao Chang, and Prof. Sujian Li. I also served as a research assistant in Microsoft Research Asia, working with Chin-yew Lin, Lintao Zhang, and Qi Chen. During my PhD period, I majored in information extraction and text generation. I have published many first-author papers in top conferences of NLP, such as ACL, EMNLP, NAACL, AAAI, etc. My research interest focus on natural language understanding and controllable text generation.
Selected Publications
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Associative Memories via Predictive Coding
Tommaso Salvatori‚ Yuhang Song‚ Yujian Hong‚ Simon Frieder‚ Lei Sha‚ Zhenghua Xu‚ Rafal Bogacz and Thomas Lukasiewicz
In Proceedings of the 35th Annual Conference on Neural Information Processing Systems‚ NeurIPS 2021. December, 2021.
Details about Associative Memories via Predictive Coding | BibTeX data for Associative Memories via Predictive Coding | Link to Associative Memories via Predictive Coding
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Controlling Text Edition by Changing Answers of Specific Questions
Lei Sha‚ Patrick Hohenecker and Thomas Lukasiewicz
In Findings of ACL. 2021.
Details about Controlling Text Edition by Changing Answers of Specific Questions | BibTeX data for Controlling Text Edition by Changing Answers of Specific Questions | Link to Controlling Text Edition by Changing Answers of Specific Questions
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Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration
Lei Sha‚ Oana−Maria Camburu and Thomas Lukasiewicz
In Kevin Leyton−Brown and Mausam, editors, Proceedings of the 35th AAAI Conference on Artificial Intelligence‚ AAAI 2021‚ Virtual Conference‚ February 2–9‚ 2021. AAAI Press. 2021.
Details about Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration | BibTeX data for Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration | Link to Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration