Abstract
We investigate the task of assessing sentencelevel prompt relevance in learner essays. Various systems using word overlap, neural embeddings and neural compositional models are evaluated on two datasets of learner writing. We propose a new method for sentencelevel similarity calculation, which learns to adjust the weights of pre-trained word embeddings for a specific task, achieving substantially higher accuracy compared to other relevant baselines.
Cite
CITATION STYLE
Rei, M., & Cummins, R. (2016). Sentence similarity measures for fine-grained estimation of topical relevance in learner essays. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 (pp. 283–288). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0533
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
