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.

本文提出了一种使用句法分析树的改进型 Word Mover's Distance（SynWMD）方法，它建立在基于句子的句法分析树中的词共现统计上，并考虑了每个单词的重要性和它们之间的局部句法结构，通过在 6 种文本语义相似度（STS）数据集和 4 个句子分类数据集上的实验证明 SynWMD 在提高评估精度方面取得了重大突破。

SynWMD：一种用于句子相似度评估的基于语法的词移距离算法

SynWMD: Syntax-aware Word Mover's Distance for Sentence Similarity Evaluation

Identifying implicit discourse relations between text spans is a challenging
task because it requires understanding the meaning of the text. To tackle this
task, recent studies have tried several deep learning methods but few of them
exploited the syntactic information. In this work, we explore the idea of
incorporating syntactic parse tree into neural networks. Specifically, we
employ the Tree-LSTM model and Tree-GRU model, which are based on the tree
structure, to encode the arguments in a relation. Moreover, we further leverage
the constituent tags to control the semantic composition process in these
tree-structured neural networks. Experimental results show that our method
achieves state-of-the-art performance on PDTB corpus.

本研究使用 Tree-LSTM 模型和 Tree-GRU 模型将句法分析树加入神经网络以编码关系中的参数，同时利用组成标记控制这些树状神经网络中的语义组合过程，实验结果表明我们的方法在 PDTB 语料库中达到了最先进的性能。