A Generative Parser with a Discriminative Recognition Algorithm

Jianpeng Cheng, Adam Lopez, Mirella Lapata


Abstract
Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for parsing and language modeling which marries a generative model with a discriminative recognition model in an encoder-decoder setting. We provide interpretations of the framework based on expectation maximization and variational inference, and show that it enables parsing and language modeling within a single implementation. On the English Penn Treen-bank, our framework obtains competitive performance on constituency parsing while matching the state-of-the-art single-model language modeling score.
Anthology ID:
P17-2019
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
118–124
Language:
URL:
https://aclanthology.org/P17-2019
DOI:
10.18653/v1/P17-2019
Bibkey:
Cite (ACL):
Jianpeng Cheng, Adam Lopez, and Mirella Lapata. 2017. A Generative Parser with a Discriminative Recognition Algorithm. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 118–124, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
A Generative Parser with a Discriminative Recognition Algorithm (Cheng et al., ACL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/P17-2019.pdf
Video:
 https://vimeo.com/234957682
Code
 cheng6076/virnng