Improving Compositional Generalization in Classification Tasks via Structure Annotations

• URL: http://arxiv.org/abs/2106.10434v1
• Date: Sat, 19 Jun 2021 06:07:27 GMT
• Title: Improving Compositional Generalization in Classification Tasks via Structure Annotations
• Authors: Juyong Kim, Pradeep Ravikumar, Joshua Ainslie, Santiago Onta\~n\'on
• Abstract summary: Humans have a great ability to generalize compositionally, but state-of-the-art neural models struggle to do so. First, we study ways to convert a natural language sequence-to-sequence dataset to a classification dataset that also requires compositional generalization. Second, we show that providing structural hints (specifically, providing parse trees and entity links as attention masks for a Transformer model) helps compositional generalization.
• Score: 33.90268697120572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. Although humans seem to have a great ability to generalize compositionally, state-of-the-art neural models struggle to do so. In this work, we study compositional generalization in classification tasks and present two main contributions. First, we study ways to convert a natural language sequence-to-sequence dataset to a classification dataset that also requires compositional generalization. Second, we show that providing structural hints (specifically, providing parse trees and entity links as attention masks for a Transformer model) helps compositional generalization.

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