2022 NAACL NAACL 2022

Generation of Synthetic Error Data of Verb Order Errors for Swedish

Abstract

AbstractWe report on our work-in-progress to generate a synthetic error dataset for Swedish by replicating errors observed in the authentic error annotated dataset. We analyze a small subset of authentic errors, capture regular patterns based on parts of speech, and design a set of rules to corrupt new data. We explore the approach and identify its capabilities, advantages and limitations as a way to enrich the existing collection of error-annotated data. This work focuses on word order errors, specifically those involving the placement of finite verbs in a sentence.

🌉 Interdisciplinary Bridge — Interdisciplinary and Mathematics & Optimization
🧭 Keyword Pioneer — synthetic error datum
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio