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Found 4 papers, 2 papers with code
PIZZA: A new benchmark for complex end-to-end task-oriented parsing
Much recent work in task-oriented parsing has focused on finding a middle ground between flat slots and intents, which are inexpressive but easy to annotate, and powerful representations such as the lambda calculus, which are expressive but costly to annotate.
Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing
However, a single model is still typically trained and deployed for each task separately, requiring labeled training data for each, which makes it challenging to support new tasks, even within a single business vertical (e. g., food-ordering or travel booking).
Training Naturalized Semantic Parsers with Very Little Data
Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant.
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models
We examine two such promising techniques, prefix tuning and bias-term tuning, specifically on semantic parsing.
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