Papers with Bayesian models
Deep Bayesian Natural Language Processing (P19-4)
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| Challenge: | Introduction to deep Bayesian learning for natural language addresses the fundamentals of statistical models and neural networks. |
| Approach: | This tutorial addresses the advances in deep Bayesian learning for natural language . it focuses on advanced Bayessian models and deep models . authors present case studies and domain applications to tackle different issues . |
| Outcome: | This tutorial focuses on advanced Bayesian models and deep models for natural language . case studies and domain applications are presented to tackle different issues in deep Bayessian processing, learning and understanding. |
Joint Word and Morpheme Segmentation with Bayesian Non-Parametric Models (2023.findings-eacl)
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| Challenge: | Language documentation often requires segmenting transcriptions of utterances into words and morphemes . a long tradition of nonparametric Bayesian models is used to handle these tasks . |
| Approach: | They propose a Bayesian model for simultaneously segmenting utterances at two levels . they use two under-resourced languages to better understand the value of weak supervision . |
| Outcome: | The proposed model can be used to identify language documents with weak supervision. |
A SMART Mnemonic Sounds like “Glue Tonic”: Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick (2024.emnlp-main)
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Nishant Balepur, Matthew Shu, Alexander Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, Jordan Boyd-Graber
| Challenge: | a new study shows that mnemonics are not effective at matching student learning to a standardized learning model. |
| Approach: | They build a keyword mnemonic generator that finds mnemonics students favor in a flashcard app . they use expressed and observed preferences to find out what students think is helpful . |
| Outcome: | The proposed mnemonics outperform existing models in keyword mnemonics . the human writer outperformed both models in terms of keyword simplicity and explanation quality . |