Neural Unsupervised Dependency Parsing

Thursday, October 31, 2019, 11:15 am - 12:15 pm PDTiCal
CR# 1135
This event is open to the public.
Recruitment Seminar
Wenjuan Han (ShanghaiTech University/UCLA)
Video Recording:

Abstract: Dependency parsing, as an essential task in Natural Language Processing, is a key step in analyzing and understanding texts. Most of the previous work on unsupervised dependency parsing is based on generative models. In order to effectively induce a grammar, various knowledge priors and inductive biases are manually encoded in the learning process. However, these knowledge priors and inductive biases are mostly local features that can only be defined by experts. Another disadvantage of generative models comes from the context-freeness, which limits the information available to dependencies in a sentence. We proposed several approaches to unsupervised dependency parsing that automatically capture useful information: correlations between tokens, context information and multilingual similarity.

Bio:I am now a visiting student in UCLA and expected to graduate in January 2020. I will get the PHD Degree at ShanghaiTech University, where I was advised by Kewei Tu. I did my bachelors at the Nanjing University of Posts and Telecommunications. My current research focuses on the study of probabilistic/neural models and follows two researching paths: (1) grammar-based representation, inference, and unsupervised learning; and (2) the application of unsupervised learning approaches with hidden variables in a variety of artificial intelligence areas including grammar induction, POS induction and perceptual grouping.

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