ACL 2023short1 citations
Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints
Abstract
Neural QCFG is a grammar-based sequence-to-sequence model with strong inductive biases on hierarchical structures. It excels in interpretability and generalization but suffers from expensive inference. In this paper, we study two low-rank variants of Neural QCFG for faster inference with different trade-offs between efficiency and expressiveness. Furthermore, utilizing the symbolic interface provided by the grammar, we introduce two soft constraints over tree hierarchy and source coverage. We experiment with various datasets and find that our models outperform vanilla Neural QCFG in most settings.
BibTeX
@inproceedings{lou-tu-2023-improving,
title = "Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints",
author = "Lou, Chao and
Tu, Kewei",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.163/",
doi = "10.18653/v1/2023.acl-short.163",
pages = "1918--1929"
}