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We investigate the task of assessing sentence-level prompt relevance inlearner essays. Various systems using word overlap, neural embeddings andneural compositional models are evaluated on two datasets of learner writing.We propose a new method for sentence-level similarity calculation, which learnsto adjust the weights of pre-trained word embeddings for a specific task,achieving substantially higher accuracy compared to other relevant baselines.

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