Making Transformers Solve Compositional Tasks
Santiago Ontanon, Joshua Ainslie, Zachary Fisher, Vaclav Cvicek
Abstract
Several studies have reported the inability of Transformer models to generalize compositionally, a key type of generalization in many NLP tasks such as semantic parsing. In this paper we explore the design space of Transformer models showing that the inductive biases given to the model by several design decisions significantly impact compositional generalization. We identified Transformer configurations that generalize compositionally significantly better than previously reported in the literature in many compositional tasks. We achieve state-of-the-art results in a semantic parsing compositional generalization benchmark (COGS), and a string edit operation composition benchmark (PCFG).
BibTeX
@inproceedings{ontanon-etal-2022-making,
title = "Making Transformers Solve Compositional Tasks",
author = "Ontanon, Santiago and
Ainslie, Joshua and
Fisher, Zachary and
Cvicek, Vaclav",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.251/",
doi = "10.18653/v1/2022.acl-long.251",
pages = "3591--3607"
}