Natural Language Processing Seminar|
Towards Open-domain Generation of Programs from Natural Language
Carnegie Mellon University
Tuesday, November 27, 2018|
4:00pm - 5:30pm
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About the Event
Code generation from natural language is the task of generating programs written in a programming language (e.g. Python) given a command in natural language (e.g. English). For example, if the input is "sort list x in reverse order", then the system would be required to output "x.sort(reverse=True)" in Python. In this talk, I will talk about (1) machine learning models to perform this code generation, (2) methods for mining data from programming web sites such as stack overflow, and (3) methods for semi-supervised learning, that allow the model to learn from either English or Python on its own, without the corresponding parallel data.
Graham Neubig is an assistant professor at the Language Technologies Institute of Carnegie Mellon University. His work focuses on natural language processing, specifically multi-lingual models that work in many different languages, and natural language interfaces that allow humans to communicate with computers in their own language. Much of this work relies on machine learning to create these systems from data, and he is also active in developing methods and algorithms for machine learning over natural language data. He publishes regularly in the top venues in natural language processing, machine learning, and speech, and his work occasionally wins awards such as best papers at EMNLP, EACL, and WNMT. He is also active in developing open-source software, and is the main developer of the DyNet neural network toolkit.
Faculty Sponsor: Rada Mihalcea
Open to: Public