most citedReverse Engineering Configurations of Neural Text Generation Models

3 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2020

StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

Yikang Shen, Yi Tay, Che Zheng +3

There are two major classes of natural language grammar -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the…

cs.IR2020

Surprise: Result List Truncation via Extreme Value Theory

Dara Bahri, Che Zheng, Yi Tay +2

Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user. The problem of…

cs.CL2020

Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study

Dara Bahri, Yi Tay, Che Zheng +3

Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning. Our work i…

cs.IR2020

Choppy: Cut Transformer For Ranked List Truncation

Dara Bahri, Yi Tay, Che Zheng +2

Work in information retrieval has traditionally focused on ranking and relevance: given a query, return some number of results ordered by relevance to the user. However, the proble…

cs.CL20203 cited

Reverse Engineering Configurations of Neural Text Generation Models

Yi Tay, Dara Bahri, Che Zheng +3

This paper seeks to develop a deeper understanding of the fundamental properties of neural text generations models. The study of artifacts that emerge in machine generated text as…