Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays
arXiv:1606.03144 · doi:10.18653/v1/W16-0533
Abstract
We investigate the task of assessing sentence-level 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 sentence-level 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.
Accepted for publication at BEA-2016