activity
20152022
most citedDeconvolutional Paragraph Representation Learning

65 citations · 207 across the 14 of their papers we have counts for

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Showing cs.CLShow all

11 papers · 1 filter

cs.CL202211 cited

NN-NER: Named Entity Recognition with Nearest Neighbor Search

Shuhe Wang, Xiaoya Li, Yuxian Meng +4

Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization,khandelwal2020nearest,meng2021gnn}, in this paper, we introduce a nearest…

cs.CL20206 cited

Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage

Shijing Si, Rui Wang, Jedrek Wosik +5

Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models. So motivated, we propose a novel framework…

cs.CL201937 cited

Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding

Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang +4

Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks. The Transformer, for instance, is an illustrative example that generates…

cs.CL2019

Learning Word Embeddings with Domain Awareness

Guoyin Wang, Yan Song, Yue Zhang +1

Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge. This can lead to unsatisfactory…

cs.CL20196 cited

Improving Textual Network Embedding with Global Attention via Optimal Transport

Liqun Chen, Guoyin Wang, Chenyang Tao +6

Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimens…

cs.CL201956 cited

Topic-Guided Variational Autoencoders for Text Generation

Wenlin Wang, Zhe Gan, Hongteng Xu +5

We propose a topic-guided variational autoencoder (TGVAE) model for text generation. Distinct from existing variational autoencoder (VAE) based approaches, which assume a simple Ga…