Recent literature suggests that averaged word vectors followed by simple
post-processing outperform many deep learning methods on semantic textual
similarity tasks. Furthermore, when averaged word vectors are trained
supervised on large corpora of paraphrases, they achieve state-of-the-art
results on standard STS benchmarks. Inspired by these insights, we push the
limits of word embeddings even further. We propose a novel fuzzy bag-of-words
(FBoW) representation for text that contains all the words in the vocabulary
simultaneously but with different degrees of membership, which are derived from
similarities between word vectors. We show that max-pooled word vectors are
only a special case of fuzzy BoW and should be compared via fuzzy Jaccard index
rather than cosine similarity. Finally, we propose DynaMax, a completely
unsupervised and non-parametric similarity measure that dynamically extracts
and max-pools good features depending on the sentence pair. This method is both
efficient and easy to implement, yet outperforms current baselines on STS tasks
by a large margin and is even competitive with supervised word vectors trained
to directly optimise cosine similarity.

本文提出了一种基于模糊词袋 (FBoW) 表示的文本相似度计算方法，通过使用词向量相似度进行加权处理，动态提取和最大池化好的特征，它是一种完全无监督和非参数化的相似度计算方法，不但比当前基线方法优秀，而且在标准 STS 基准测试中，与直接优化余弦相似度的监督式词向量训练相竞争。