CLASP: Few-shot cross-lingual data augmentation for semantic parsing
A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of
human-labeled training data. Given the complexity and cost of human annotation for SP …
human-labeled training data. Given the complexity and cost of human annotation for SP …
A survey of intent classification and slot-filling datasets for task-oriented dialog
Interest in dialog systems has grown substantially in the past decade. By extension, so too
has interest in developing and improving intent classification and slot-filling models, which …
has interest in developing and improving intent classification and slot-filling models, which …
Compositional task-oriented parsing as abstractive question answering
Task-oriented parsing (TOP) aims to convert natural language into machine-readable
representations of specific tasks, such as setting an alarm. A popular approach to TOP is to …
representations of specific tasks, such as setting an alarm. A popular approach to TOP is to …
Unfreeze with care: Space-efficient fine-tuning of semantic parsing models
Semantic parsing is a key NLP task that maps natural language to structured meaning
representations. As in many other NLP tasks, SOTA performance in semantic parsing is now …
representations. As in many other NLP tasks, SOTA performance in semantic parsing is now …
Cross-TOP: Zero-shot cross-schema task-oriented parsing
M Rubino, NG Mesnards, U Shah, N Jiang… - arXiv preprint arXiv …, 2022 - arxiv.org
Deep learning methods have enabled task-oriented semantic parsing of increasingly
complex utterances. However, a single model is still typically trained and deployed for each …
complex utterances. However, a single model is still typically trained and deployed for each …