Papers with LaSyn

2 papers
Latent Part-of-Speech Sequences for Neural Machine Translation (D19-1)

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Challenge: Existing methods for learning target side syntactic structure are greedy and only allow them to explore a limited portion of the latent space.
Approach: They propose a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics while allowing for effective inference over the latent space.
Outcome: The proposed model captures the co-dependence between syntax and semantics while allowing for efficient inference over the latent space.
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis (2023.findings-emnlp)

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Challenge: Latent Synthesis is an efficient textual data utilization framework for end-to-end speech processing models . labeled speech data are scarcer and more expensive for collection compared to textual ones .
Approach: They propose a textual data utilization framework for E2E speech processing models . they train a latent synthesizer to convert textual information into an intermediate latent representation .
Outcome: The proposed framework improves on low-resource speech recognition and spoken language understanding tasks.

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