Question Answering Resources Applied to Slot-Filling.
Year: 2018
Type: preprint
Abstract: We investigate the utility of pre-existing question answering models and data for a recently proposed relation extraction task. We find that in the low-resource and zero-shot cases, such resources are surprisingly useful. Moreover, the resulting models show robust performance on a new test set we create from the task's original datasets.
Source: arXiv (Cornell University)
Authors: Jeff Mitchell, Sebastian Riedel
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2799218064