Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays

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.