Political Event Coding as Text-to-Text Sequence Generation
Yaoyao Dai, Benjamin J. Radford, Andrew Halterman · 2022
We report on the current status of an effort to produce political event data from unstructured text via a Transformer language model.Compelled by the current lack of publicly available and up-to-date event coding software, we seek to train a model that can produce structured political event records at the sentence level.Our approach differs from previous efforts in that we conceptualize this task as one of text-to-text sequence generation.We motivate this choice by outlining desirable properties of text generation models for the needs of event coding.To overcome the lack of sufficient training data, we also describe a method for generating synthetic text and event record pairs that we use to fit our model.