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researcher

Steven Cao

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California.

6 papers hereh-index 10806 citations20 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author2

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CR1
  • eess.IV1
affiliations
  • Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California.
same name
  • Steven Cao — 1 paper
  • Steven Cao — 1 paper
  • Steven Cao — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182021
most citedMultilingual Alignment of Contextual Word Representations

152 citations · 162 across the 4 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2021★ 1 cited

Low-Complexity Probing via Finding Subnetworks

Steven Cao, Victor Sanh, Alexander M. Rush

The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model's internal representations. Th…

cs.CL2020

Unsupervised Parsing via Constituency Tests

Steven Cao, Nikita Kitaev, Dan Klein

We propose a method for unsupervised parsing based on the linguistic notion of a constituency test. One type of constituency test involves modifying the sentence via some transform…

cs.CL2020★ 152 cited

Multilingual Alignment of Contextual Word Representations

Steven Cao, Nikita Kitaev, Dan Klein

We propose procedures for evaluating and strengthening contextual embedding alignment and show that they are useful in analyzing and improving multilingual BERT. In particular, aft…

cs.CL2018

Multilingual Constituency Parsing with Self-Attention and Pre-Training

Nikita Kitaev, Steven Cao, Dan Klein

We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no…

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