Disentangling Representations of Text by Masking Transformers

@article{Zhang2021DisentanglingRO,
  title={Disentangling Representations of Text by Masking Transformers},
  author={Xiongyi Zhang and Jan-Willem van de Meent and Byron C. Wallace},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.07155}
}
Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a range of downstream tasks. In this paper we explore whether it is possible to learn disentangled representations by identifying existing subnetworks within pretrained models that encode distinct, complementary aspects. Concretely, we learn binary masks over transformer weights or hidden units to uncover subsets of features that correlate… 
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