521 citations · 921 across the 26 of their papers we have counts for
5 papers · 1 filter
Improving Compositional Generalization with Latent Structure and Data Augmentation
Linlu Qiu, Peter Shaw, Panupong Pasupat +4
Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization. Compositional data augmentation via example recombination has…
Controllable Semantic Parsing via Retrieval Augmentation
Panupong Pasupat, Yuan Zhang, Kelvin Guu
In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its pr…
Graph-Based Decoding for Task Oriented Semantic Parsing
Jeremy R. Cole, Nanjiang Jiang, Panupong Pasupat +2
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In…
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations
Jonathan Herzig, Peter Shaw, Ming-Wei Chang +3
Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization. While specialized mode…
Few-shot Intent Classification and Slot Filling with Retrieved Examples
Dian Yu, Luheng He, Yuan Zhang +3
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce…