  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.

Computation and Language;Machine Learning;Neural and Evolutionary Computing