Search Results for author: Irene Li

Found 22 papers, 9 papers with code

Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution

no code implementations ACL 2022 Irene Li, Linfeng Song, Kun Xu, Dong Yu

Coreference resolution over semantic graphs like AMRs aims to group the graph nodes that represent the same entity.

Coreference Resolution

Improving Cross-lingual Text Classification with Zero-shot Instance-Weighting

no code implementations ACL (RepL4NLP) 2021 Irene Li, Prithviraj Sen, Huaiyu Zhu, Yunyao Li, Dragomir Radev

In this paper, we propose zero-shot instance-weighting, a general model-agnostic zero-shot learning framework for improving CLTC by leveraging source instance weighting.

Text Classification Zero-Shot Learning

EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts

1 code implementation13 Apr 2022 Irene Li, Keen You, Xiangru Tang, Yujie Qiao, Lucas Huang, Chia-Chun Hsieh, Benjamin Rosand, Dragomir Radev

The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research.

Information Retrieval Machine Translation +2

Surfer100: Generating Surveys From Web Resources, Wikipedia-style

no code implementations13 Dec 2021 Irene Li, Alexander Fabbri, Rina Kawamura, Yixin Liu, Xiangru Tang, Jaesung Tae, Chang Shen, Sally Ma, Tomoe Mizutani, Dragomir Radev

Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely.

Language Modelling

Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains

no code implementations17 Sep 2021 Irene Li, Vanessa Yan, Dragomir Radev

Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator.

Link Prediction

Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders

no code implementations ACL 2021 Irene Li, Vanessa Yan, Tianxiao Li, Rihao Qu, Dragomir Radev

For example, one may be an expert in the natural language processing (NLP) domain but want to determine the best order to learn new concepts in an unfamiliar Computer Vision domain (CV).

R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning

1 code implementation COLING 2020 Irene Li, Alexander Fabbri, Swapnil Hingmire, Dragomir Radev

The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs.


A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation

1 code implementation30 Oct 2019 Irene Li, Michihiro Yasunaga, Muhammed Yavuz Nuzumlali, Cesar Caraballo, Shiwani Mahajan, Harlan Krumholz, Dragomir Radev

Specifically, a neural topic-attention model is applied to learn improved contextualized sentence representations for medical term abbreviation disambiguation.

Few-Shot Learning

ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks

1 code implementation4 Sep 2019 Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R. Fabbri, Irene Li, Dan Friedman, Dragomir R. Radev

Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community.

Scientific Document Summarization

SParC: Cross-Domain Semantic Parsing in Context

5 code implementations ACL 2019 Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev

The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.

Semantic Parsing Text-To-Sql

What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

1 code implementation26 Nov 2018 Irene Li, Alexander R. Fabbri, Robert R. Tung, Dragomir R. Radev

The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation.

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