Papers by Yushu Yang

2 papers
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)

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Challenge: Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge.
Approach: They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly.
Outcome: The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection.
Rethinking Token Reduction for State Space Models (2024.emnlp-main)

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Challenge: Existing methods for token reduction for SSMs lead to performance drops . a recent study shows that Mamba-2 improves the accuracy of the model by 5.7% to 13.1% .
Approach: They propose a token reduction method that integrates token importance and similarity into SSMs and takes advantage of pruning and merging.
Outcome: The proposed method improves accuracy by 5.7% to 13.1% on six benchmarks with Mamba-2 compared to existing methods while reducing computational demands and memory requirements.

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