Template Filling with Generative Transformers
Xinya Du, Alexander Rush, Claire Cardie
Abstract
Template filling is generally tackled by a pipeline of two separate supervised systems – one for role-filler extraction and another for template/event recognition. Since pipelines consider events in isolation, they can suffer from error propagation. We introduce a framework based on end-to-end generative transformers for this task (i.e., GTT). It naturally models the dependence between entities both within a single event and across the multiple events described in a document. Experiments demonstrate that this framework substantially outperforms pipeline-based approaches, and other neural end-to-end baselines that do not model between-event dependencies. We further show that our framework specifically improves performance on documents containing multiple events.
BibTeX
@inproceedings{du-etal-2021-template,
title = "Template Filling with Generative Transformers",
author = "Du, Xinya and
Rush, Alexander and
Cardie, Claire",
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.70/",
doi = "10.18653/v1/2021.naacl-main.70",
pages = "909--914"
}