Papers by Jinxi Liu

2 papers
Causality-aware Concept Extraction based on Knowledge-guided Prompting (2023.acl-long)

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Challenge: Concepts in knowledge graphs (KGs) are far from complete in existing knowledge graph models.
Approach: They propose to equip a PLM-based extractor with a knowledge-guided prompt to alleviate concept bias by removing spurious co-occurrence correlations from existing knowledge.
Outcome: The proposed prompt can alleviate concept bias and improve the performance of existing models.
Generative Entity Typing with Curriculum Learning (2022.emnlp-main)

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Challenge: Entity typing fails to assign an entity to the types beyond the predefined type set.
Approach: They propose a generative entity typing paradigm that assigns types to entities . traditional classification-based approaches fail to assign entities to the types beyond the predefined set . they employ curriculum learning to train the model on heterogeneous data .
Outcome: The proposed model outperforms the state-of-the-art model on heterogeneous training data.

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