论文标题
通过利用自我发挥矩阵来改善单词搬运工的距离
Improving word mover's distance by leveraging self-attention matrix
论文作者
论文摘要
测量两个句子之间的语义相似性仍然是一项重要任务。单词移动器的距离（WMD）通过单词嵌入式集合之间的最佳对齐来计算相似性。但是，WMD不利用单词顺序，即使在语义上截然不同，可以区分具有相似单词的重叠句子的句子具有挑战性。在这里，我们试图通过合并由Bert的自我发作矩阵（SAM）代表的句子结构来改善WMD。所提出的方法基于融合的Gromov-Wasserstein距离，该距离同时考虑了嵌入一词的相似性和用于计算两个句子之间的最佳传输的SAM。实验证明了所提出的方法可以增强WMD及其在释义识别中的变体，并在语义文本相似性中具有近乎等效的性能。我们的代码可在\ url {https://github.com/ymgw55/wsmd}上找到。
Measuring the semantic similarity between two sentences is still an important task. The word mover's distance (WMD) computes the similarity via the optimal alignment between the sets of word embeddings. However, WMD does not utilize word order, making it challenging to distinguish sentences with significant overlaps of similar words, even if they are semantically very different. Here, we attempt to improve WMD by incorporating the sentence structure represented by BERT's self-attention matrix (SAM). The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences. Experiments demonstrate the proposed method enhances WMD and its variants in paraphrase identification with near-equivalent performance in semantic textual similarity. Our code is available at \url{https://github.com/ymgw55/WSMD}.
