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Detection of semantic similarity plays a vital role in sentence matching. Itrequires to learn discriminative representations of natural language. Recently,owing to more and more sophisticated model architecture, impressive progresshas been made, along with a time-consuming training process andnot-interpretable inference. To alleviate this problem, we explore a metriclearning approach, named FILM (Fast, Interpretable, and Low-rank Metriclearning) to efficiently find a high discriminative projection of thehigh-dimensional data. We construct this metric learning problem as a manifoldoptimization problem and solve it with the Cayley transformation method withthe Barzilai-Borwein step size. In experiments, we apply FILM with triplet lossminimization objective to the Quora Challenge and Semantic Textual Similarity(STS) Task. The results demonstrate that the FILM method achieves superiorperformance as well as the fastest computation speed, which is consistent withour theoretical analysis of time complexity.

FILM: A Fast, Interpretable, and Low-rank Metric Learning Approach for Sentence Matching