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

2020-10-12 08:24:41
Xiangru Tang, Alan Aw
arXiv_AI
         
Abstract

Detection of semantic similarity plays a vital role in sentence matching. It requires to learn discriminative representations of natural language. Recently, owing to more and more sophisticated model architecture, impressive progress has been made, along with a time-consuming training process and not-interpretable inference. To alleviate this problem, we explore a metric learning approach, named FILM(Fast, Interpretable, and Low-rank Metric learning) to efficiently find a high discriminative projection of the high-dimensional data. We construct this metric learning problem as a manifold optimization problem and solve it with the Cayleytransformation method with the Barzilai-Borweinstep size. In experiments, we applyFILMwith triplet loss minimization objective to theQuora Challenge and Semantic Textual Similarity (STS) Task. The results demonstrate that the FILM method achieves superior performance as well as the fastest computation speed, which is consistent with our theoretical analysis of time complexity.

Abstract (translated)

URL

https://arxiv.org/abs/2010.05523

PDF

https://arxiv.org/pdf/2010.05523.pdf

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