Sentence Similarity Measures for Fine-Grained Estimation of Topical   Relevance in Learner Essays
  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. 
Marek Rei and Ronan Cummins
cs.CL cs.LG cs.NE