Multi-Source Domain Adaptation for Text Classification via
DistanceNet-Bandits
- URL: http://arxiv.org/abs/2001.04362v3
- Date: Tue, 3 Mar 2020 21:21:22 GMT
- Title: Multi-Source Domain Adaptation for Text Classification via
DistanceNet-Bandits
- Authors: Han Guo, Ramakanth Pasunuru, Mohit Bansal
- Abstract summary: We present a study of various distance-based measures in the context of NLP tasks, that characterize the dissimilarity between domains based on sample estimates.
We develop a DistanceNet model which uses these distance measures as an additional loss function to be minimized jointly with the task's loss function.
We extend this model to a novel DistanceNet-Bandit model, which employs a multi-armed bandit controller to dynamically switch between multiple source domains.
- Score: 101.68525259222164
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Domain adaptation performance of a learning algorithm on a target domain is a
function of its source domain error and a divergence measure between the data
distribution of these two domains. We present a study of various distance-based
measures in the context of NLP tasks, that characterize the dissimilarity
between domains based on sample estimates. We first conduct analysis
experiments to show which of these distance measures can best differentiate
samples from same versus different domains, and are correlated with empirical
results. Next, we develop a DistanceNet model which uses these distance
measures, or a mixture of these distance measures, as an additional loss
function to be minimized jointly with the task's loss function, so as to
achieve better unsupervised domain adaptation. Finally, we extend this model to
a novel DistanceNet-Bandit model, which employs a multi-armed bandit controller
to dynamically switch between multiple source domains and allow the model to
learn an optimal trajectory and mixture of domains for transfer to the
low-resource target domain. We conduct experiments on popular sentiment
analysis datasets with several diverse domains and show that our DistanceNet
model, as well as its dynamic bandit variant, can outperform competitive
baselines in the context of unsupervised domain adaptation.
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