论文标题
电影：一种匹配句子的快速，易于解释和低级的度量学习方法
FILM: A Fast, Interpretable, and Low-rank Metric Learning Approach for Sentence Matching
论文作者
论文摘要
语义相似性的检测在句子匹配中起着至关重要的作用。它需要学习自然语言的歧视性表示。最近，由于越来越复杂的模型体系结构，已经取得了令人印象深刻的进步，以及耗时的培训过程以及不可解开的推论。为了减轻这个问题，我们探索了一种指标学习方法，命名为电影（快速，可解释和低级度量学习），以有效地找到对高维数据的高歧视性投影。我们将此度量学习问题构建为一种多种优化问题，并使用Barzilai-Borwein步骤大小的Cayley Transformation方法来解决它。在实验中，我们将带有三重损失最小化目标的膜应用于Quora挑战和语义文本相似性（STS）任务。结果表明，膜方法可以达到卓越的性能以及最快的计算速度，这与我们对时间复杂性的理论分析一致。
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 Cayley transformation method with the Barzilai-Borwein step size. In experiments, we apply FILM with triplet loss minimization objective to the Quora 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.
