Papers with model building

2 papers
Resisting the Lure of the Skyline: Grounding Practices in Active Learning for Morphological Inflection (2024.acl-short)

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Challenge: Several approaches to active learning are available, including confidence-based, diversity-based and committee-based.
Approach: They propose to use a baseline and a skyline to measure the accuracy of the unannotated sample pool.
Outcome: The proposed model outperforms a random selection baseline and a skyline approach.
Incorporating Priors with Feature Attribution on Text Classification (P19-1)

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Challenge: Feature attribution methods are used to help users interpret complex models.
Approach: They propose a feature attribution method that integrates feature attributed features into the objective function to allow machine learning practitioners to incorporate priors in model building.
Outcome: The proposed method reduces undesired model biases without a tradeoff on the original task and improves classifier performance in scarce data setting.

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