Papers with Bayesian generative model

2 papers
Sentiment as an Ordinal Latent Variable (2023.eacl-main)

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Challenge: Existing dictionaries are limited in coverage and sentiment scales vary widely; some are discrete others continuous.
Approach: They propose a Bayesian generative model that learns a composite sentiment dictionary as an interpolation between six existing dictionaries with different scales.
Outcome: The proposed model learns a composite sentiment dictionary as an interpolation between six existing dictionaries with different scales.
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages (2021.tacl-1)

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Challenge: Currently, there are only 24 languages in the world that have not been annotated . transferring knowledge across domains is a common solution .
Approach: They propose a Bayesian generative model for the space of neural parameters that factorizes into latent variables for each language and each task.
Outcome: The proposed model can perform better than state-of-the-art methods with a typologically diverse sample of 33 languages from 4 continents and 11 families.

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