Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification

10/29/2019
by   Pratik Kayal, et al.
0

The task of learning a sentiment classification model that adapts well to any target domain, different from the source domain, is a challenging problem. Majority of the existing approaches focus on learning a common representation by leveraging both source and target data during training. In this paper, we introduce a two-stage training procedure that leverages weakly supervised datasets for developing simple lift-and-shift-based predictive models without being exposed to the target domain during the training phase. Experimental results show that transfer with weak supervision from a source domain to various target domains provides performance very close to that obtained via supervised training on the target domain itself.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/16/2022

GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

This paper studies weakly supervised domain adaptation(WSDA) problem, wh...
research
09/03/2021

Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition

The domain shift between the source and target domain is the main challe...
research
02/01/2020

Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning

Domain-adapted sentiment classification refers to training on a labeled ...
research
03/25/2019

Weakly-Supervised Unconstrained Action Unit Detection via Feature Disentanglement

Facial action unit (AU) detection in the wild is a challenging problem, ...
research
10/06/2020

Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images

Although deep learning has achieved remarkable successes over the past y...
research
10/18/2021

Unsupervised Finetuning

This paper studies "unsupervised finetuning", the symmetrical problem of...
research
03/31/2021

PhyAug: Physics-Directed Data Augmentation for Deep Sensing Model Transfer in Cyber-Physical Systems

Run-time domain shifts from training-phase domains are common in sensing...

Please sign up or login with your details

Forgot password? Click here to reset