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Found 6 papers, 3 papers with code
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance.
Schema-Aware Multi-Task Learning for Complex Text-to-SQL
Conventional text-to-SQL parsers are not good at synthesizing complex SQL queries that involve multiple tables or columns, due to the challenges inherent in identifying the correct schema items and performing accurate alignment between question and schema items.
TLM: Token-Level Masking for Transformers
Structured dropout approaches, such as attention dropout and DropHead, have been investigated to regularize the multi-head attention mechanism in Transformers.
NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry
Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation.
FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration
To accomplish punctuation restoration, most existing methods focus on introducing extra information (e. g., part-of-speech) or addressing the class imbalance problem.
A Context-Aware Feature Fusion Framework for Punctuation Restoration
To accomplish the punctuation restoration task, most existing approaches focused on leveraging extra information (e. g., part-of-speech tags) or addressing the class imbalance problem.
