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Found 6 papers, 4 papers with code
Empirical Analysis for Unsupervised Universal Dependency Parse Tree Aggregation
Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks.
An Empirical Study of Using ChatGPT for Fact Verification Task
(2) What are different prompts performance using ChatGPT for fact verification tasks?
Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts
We empirically demonstrate that the top-ranked prompts are high-quality and significantly outperform the base prompt and the prompts generated using few-shot learning for the binary sentence-level sentiment classification task.
CPTAM: Constituency Parse Tree Aggregation Method
This paper adopts the truth discovery idea to aggregate constituency parse trees from different parsers by estimating their reliability in the absence of ground truth.
Truth Discovery in Sequence Labels from Crowds
The proposed Aggregation method for Sequential Labels from Crowds ($AggSLC$) jointly considers the characteristics of sequential labeling tasks, workers' reliabilities, and advanced machine learning techniques.
OptSLA: an Optimization-Based Approach for Sequential Label Aggregation
Our results show that the proposed OptSLA outperforms the state-of-the-art aggregation methods, and the results are easier to interpret.
