Papers by Xinyu Ning

2 papers
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing (2022.coling-1)

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Challenge: Experimental results show that fine-grained entity typing (FET) can be used to deduce specific semantic types of entities.
Approach: They propose a type-enriched hierarchical contrastive strategy to model type differences . their method can make type information directly perceptible and improve distinguishability .
Outcome: The proposed method can model the differences between hierarchical types and distinguish multi-grained similar types at different granularities.
DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation (2024.lrec-main)

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Challenge: Existing methods for generating abstracts involve collecting domain data and training corresponding models to complete the task of text summarization.
Approach: They propose a method to train language models based on domain datasets and a Dynamic Graph of Thought (DGoT) which inherits the advantages of existing GoT prompt approach while reducing model reasoning cost.
Outcome: The proposed method saves the cost of model training and improves reliability due to the hallucination problem of LLMs.

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