51 citations · 133 across the 15 of their papers we have counts for
6 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…
Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks
Wang Zhu, Peter Shaw, Tal Linzen +1
Neural network models often generalize poorly to mismatched domains or distributions. In NLP, this issue arises in particular when models are expected to generalize compositionally…
Visually Grounded Concept Composition
Bowen Zhang, Hexiang Hu, Linlu Qiu +2
We investigate ways to compose complex concepts in texts from primitive ones while grounding them in images. We propose Concept and Relation Graph (CRG), which builds on top of con…
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?
Linlu Qiu, Hexiang Hu, Bowen Zhang +2
We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding. First, we study which aspects of…
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…