Papers with semantic decoding

2 papers
Fully Statistical Neural Belief Tracking (P18-2)

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Challenge: Existing framework for a dialogue state tracking model requires an expensive manual retuning step .
Approach: They propose to improve existing NBT model by removing a manual retuning step . they propose two different statistical update mechanisms to improve model performance .
Outcome: The proposed model achieves competitive performance and provides a robust framework for building resource-light DST models.
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding (2021.acl-long)

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Challenge: Experimental results show that Synchronous Semantic Decoding (SSD) can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.
Approach: They propose an unsupervised method which solves the semantic gap and the structure gap by leveraging paraphrasing and grammar-constrained decoding.
Outcome: The proposed method can solve the semantic gap and structure gap on multiple datasets.

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