Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle
Yikang Shen, Shawn Tan, Alessandro Sordoni, Siva Reddy, Aaron Courville
Abstract
Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right). To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions. Experiments show that SOM can achieve strong results in language modeling, incremental parsing, and syntactic generalization tests while using fewer parameters than other models.
BibTeX
@inproceedings{shen-etal-2021-explicitly,
title = "Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle",
author = "Shen, Yikang and
Tan, Shawn and
Sordoni, Alessandro and
Reddy, Siva and
Courville, Aaron",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.132/",
doi = "10.18653/v1/2021.naacl-main.132",
pages = "1660--1672"
}