Papers with expectation-maximization
Latent-Variable Generative Models for Data-Efficient Text Classification (D19-1)
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| Challenge: | Generative classifiers offer potential advantages over discriminative classifications, including data efficiency and zero-shot learning. |
| Approach: | They introduce discrete latent variables into generative story to improve classifiers' performance . they empirically characterize performance of their models on six text classification datasets . |
| Outcome: | The proposed model outperforms discriminative and generative classifiers on six text classification datasets. |
Neural Unsupervised Reconstruction of Protolanguage Word Forms (2023.acl-long)
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| Challenge: | Existing methods for reconstructing ancient word forms use expectation-maximization . past work has used this method to predict simple phonological changes . |
| Approach: | They extend expectation-maximization to predict phonological changes between ancient word forms and their cognates in modern languages. |
| Outcome: | The proposed model reduces edit distance from the target word forms compared to previous methods. |
Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent. |
| Approach: | They propose a probabilistic model for document-level relation extraction by learning logic rules. |
| Outcome: | The proposed model outperforms baseline models in relation performance and logical consistency. |
Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of the Noisy Channel (2024.emnlp-main)
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| Challenge: | a task-oriented dialogue system requires turn-level annotations for interacting with their APIs. |
| Approach: | They propose an unsupervised approach that infers turn-level annotations as latent variables using a noisy channel model to build an end-to-end dialogue agent. |
| Outcome: | The proposed method doubles the success rate of a strong GPT-3.5 benchmark. |
Mutual-Taught for Co-adapting Policy and Reward Models (2025.acl-long)
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Tianyuan Shi, Canbin Huang, Fanqi Wan, Longguang Zhong, Ziyi Yang, Weizhou Shen, Xiaojun Quan, Ming Yan
| Challenge: | Experimental results show that this iterative approach leads to consistent improvements in both the policy model and reward model. |
| Approach: | They propose a method that iteratively improves both the policy model and reward model without requiring additional human annotation. |
| Outcome: | The proposed method improves both the policy model and reward model without human annotation. |