25 citations · 69 across the 7 of their papers we have counts for
8 papers
Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains
Irene Li, Vanessa Yan, Dragomir Radev
Prerequisite chain learning helps people acquire new knowledge efficiently. While people may quickly determine learning paths over concepts in a domain, finding such paths in other…
Detecting Bias in Transfer Learning Approaches for Text Classification
Irene Li
Classification is an essential and fundamental task in machine learning, playing a cardinal role in the field of natural language processing (NLP) and computer vision (CV). In a su…
Towards Debiasing Sentence Representations
Paul Pu Liang, Irene Mengze Li, Emily Zheng +3
As natural language processing methods are increasingly deployed in real-world scenarios such as healthcare, legal systems, and social science, it becomes necessary to recognize th…
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
Irene Li, Alexander Fabbri, Swapnil Hingmire +1
The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs. In this paper, we frame learning prer…
What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
Irene Li, Yixin Li, Tianxiao Li +3
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent. Besides direct physical and economic threats, the pandemic also indirectly i…
A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
Irene Li, Michihiro Yasunaga, Muhammed Yavuz Nuzumlalı +4
Automated analysis of clinical notes is attracting increasing attention. However, there has not been much work on medical term abbreviation disambiguation. Such abbreviations are a…
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…
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Tao Yu, Rui Zhang, Kai Yang +9
We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 uniq…