6.5k citations · 10.4k across the 118 of their papers we have counts for
7 papers · 2 filters
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning
Tian Li, Xiang Chen, Shanghang Zhang +2
Contrastive learning (CL) has been successful as a powerful representation learning method. In this paper, we propose a contrastive learning framework for cross-domain sentiment cl…
Reservoir Transformers
Sheng Shen, Alexei Baevski, Ari S. Morcos +3
We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas i…
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization
Tian Li, Xiang Chen, Shanghang Zhang +2
Contrastive learning (CL) has been successful as a powerful representation learning method. In this work we propose CLIM: Contrastive Learning with mutual Information Maximization,…
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Sicheng Zhao, Yang Xiao, Jiang Guo +5
Sentiment analysis of user-generated reviews or comments on products and services in social networks can help enterprises to analyze the feedback from customers and take correspond…
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
Forrest N. Iandola, Albert E. Shaw, Ravi Krishna +1
Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, nat…
PowerNorm: Rethinking Batch Normalization in Transformers
Sheng Shen, Zhewei Yao, Amir Gholami +2
The standard normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN). This is different than batch normalization (…