Papers with MTOP dataset

2 papers
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models (2023.emnlp-main)

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Challenge: Existing LLMs cannot generalize to domain-specific parsing tasks in a zero-shot setting.
Approach: They propose a task-oriented parsing method that decomposes parse problem into abstractive and extractive question-answering problems.
Outcome: The proposed method decomposes a parsing problem into abstractive and extractive question-answering (QA) problems.
Controllable Semantic Parsing via Retrieval Augmentation (2021.emnlp-main)

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Challenge: a mechanism for enacting behavior changes without expensive model re-training would be preferable.
Approach: They propose a controllable semantic parser that retrieves related exemplars from a retrieval index and augments them to the query.
Outcome: The proposed model can parse queries in a new domain, adapt predictions toward specified patterns, or adapt to new semantic schemas without re-training the model.

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