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arXiv · 1606.03144

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

Marek Rei·Ronan Cummins
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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.

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Marek Rei, Ronan Cummins. 2016-06-09. Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays. https://doi.org/10.18653/v1%2Fw16-0533

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