Papers by ZhanHui Kang

2 papers
TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)

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Challenge: TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications.
Approach: They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Outcome: The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions.
An Anchor-based Relative Position Embedding Method for Cross-Modal Tasks (2022.emnlp-main)

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Challenge: Position Embedding (PE) is essential for transformer to capture the sequence ordering of input tokens.
Approach: They propose a unified position embedding method that bridges the semantic gap between modalities and embeds the anchor-based distance to guide computation of cross-attention.
Outcome: The proposed method obtains new SOTA results on a wide range of benchmarks.

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