activity
20192021
most citedAssessing Social and Intersectional Biases in Contextualized Word Representations

68 citations · 88 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL20214 cited

Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models

Xin Zhou, Ruotian Ma, Tao Gui +3

Plug-and-play functionality allows deep learning models to adapt well to different tasks without requiring any parameters modified. Recently, prefix-tuning was shown to be a plug-a…

cs.CL2020

DART: Open-Domain Structured Data Record to Text Generation

Linyong Nan, Dragomir Radev, Rui Zhang +21

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when…

cs.CL2020

ESPRIT: Explaining Solutions to Physical Reasoning Tasks

Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan +7

Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for comm…

cs.CL201968 cited

Assessing Social and Intersectional Biases in Contextualized Word Representations

Yi Chern Tan, L. Elisa Celis

Social bias in machine learning has drawn significant attention, with work ranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for…

cs.CL2019

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

Tao Yu, Rui Zhang, He Yang Er +21

We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained f…

cs.CL2019

SParC: Cross-Domain Semantic Parsing in Context

Tao Yu, Rui Zhang, Michihiro Yasunaga +16

We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It…