Papers by Yookoon Park

2 papers
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)

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Challenge: Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs .
Approach: They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable.
Outcome: The proposed model outperforms state-of-the-art models on Cornell Movie Dialog and Ubuntu Dialog Corpus.
Normalized Contrastive Learning for Text-Video Retrieval (2022.emnlp-main)

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Challenge: Cross-modal contrastive learning suffers from incorrect normalization of the sum retrieval probabilities of each text or video instance.
Approach: They propose a normalized contrastive learning algorithm that normalizes the sum retrieval probabilities of each instance so that every text and video instance is fairly represented.
Outcome: Empirical results show that NCL brings significant gains in text-video retrieval on different model architectures without any architecture engineering.

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