SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation
This work addresses sentence similarity evaluation for natural language processing applications, representing an incremental improvement over existing WMD methods.
The authors tackled the problem of sentence similarity evaluation by proposing SynWMD, a syntax-aware Word Mover's Distance method that incorporates word importance and syntactic structure, achieving state-of-the-art performance on textual semantic similarity tasks and outperforming other WMD-based methods on sentence classification tasks.
Word Mover's Distance (WMD) computes the distance between words and models text similarity with the moving cost between words in two text sequences. Yet, it does not offer good performance in sentence similarity evaluation since it does not incorporate word importance and fails to take inherent contextual and structural information in a sentence into account. An improved WMD method using the syntactic parse tree, called Syntax-aware Word Mover's Distance (SynWMD), is proposed to address these two shortcomings in this work. First, a weighted graph is built upon the word co-occurrence statistics extracted from the syntactic parse trees of sentences. The importance of each word is inferred from graph connectivities. Second, the local syntactic parsing structure of words is considered in computing the distance between words. To demonstrate the effectiveness of the proposed SynWMD, we conduct experiments on 6 textual semantic similarity (STS) datasets and 4 sentence classification datasets. Experimental results show that SynWMD achieves state-of-the-art performance on STS tasks. It also outperforms other WMD-based methods on sentence classification tasks.