Template Filling with Generative Transformers
Xinya Du, Alexander M. Rush, Claire Cardie · 2021
Template filling is generally tackled by a pipeline of two separate supervised systemsone 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.