Year: 2019
Type: conference-paper
Abstract: Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering.Neural networks offer an alternative to feature engineering, but they typically require more annotated data.This paper explores network structures, contextualized embeddings and pre-training strategies aimed at capturing discourse characteristics of essays.Experiments on three essay scoring tasks show benefits from all three strategies in different combinations, with simpler architectures being more effective when less training data is available.
DOI: 10.18653/v1/w19-4450
Authors: Farah Nadeem, Huy Nguyen, Yang Liu, Mari Ostendorf
Institution: University of Washington
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W2970811405