Papers by Shakti Kumar

2 papers
From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations (2026.findings-acl)

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Challenge: DeFactoX integrates Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning.
Approach: They propose a framework that integrates Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning.
Outcome: The proposed framework combines Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning.
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.

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