Papers by Hussain Zaidi
BeamR: Beam Reweighing with Attribute Discriminators for Controllable Text Generation (2022.findings-aacl)
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| Challenge: | Recent advances in natural language processing have led to the availability of large pre-trained language models with rich generative capabilities. |
| Approach: | They propose a method to combine generative LMs with attribute discriminators to control different attributes of text generation. |
| Outcome: | The proposed method performs better than existing state-of-the-art approaches in sentiment steering and machine translation formality tasks. |
Learning Robust Latent Representations for Controllable Speech Synthesis (2021.findings-acl)
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| Challenge: | Variational Auto-Encoders (VAEs) for learning disentangled latent representations in speech fail to learn latent clusters of speaker attributes when trained on limited or noisy datasets. |
| Approach: | They propose a Variational Auto-Encoder (VAE) that minimizes mutual information between latent variables and learns controllable latent representations in speech data. |
| Outcome: | The proposed model reduces the cluster overlap of speaker attributes by 30% over LSTM-VAE. |