A key principle in assessing textual similarity is measuring the degree of
semantic overlap between two texts by considering the word alignment. Such
alignment-based approaches are intuitive and interpretable; however, they are
empirically inferior to the simple cosine similarity between general-purpose
sentence vectors. To address this issue, we focus on and demonstrate the fact
that the norm of word vectors is a good proxy for word importance, and their
angle is a good proxy for word similarity. Alignment-based approaches do not
distinguish them, whereas sentence-vector approaches automatically use the norm
as the word importance. Accordingly, we propose a method that first decouples
word vectors into their norm and direction, and then computes alignment-based
similarity using earth mover's distance (i.e., optimal transport cost), which
we refer to as word rotator's distance. Besides, we find how to grow the norm
and direction of word vectors (vector converter), which is a new systematic
approach derived from sentence-vector estimation methods. On several textual
similarity datasets, the combination of these simple proposed methods
outperformed not only alignment-based approaches but also strong baselines. The
source code is available at this https URL

本研究旨在解决文本相似性度量中的挑战，提出了一种基于词向量旋转的新方法，利用词向量的模长和角度区分词的重要性和相似性，并通过此方法，以优化输送成本方式计算对齐相似度，相较于其他传统对齐方法和基线模型更为优越。

词语旋转距离

Word Rotator's Distance

In this paper, we present a method for adversarial decomposition of text
representation. This method can be used to decompose a representation of an
input sentence into several independent vectors, each of them responsible for a
specific aspect of the input sentence. We evaluate the proposed method on two
case studies: the conversion between different social registers and diachronic
language change. We show that the proposed method is capable of fine-grained
controlled change of these aspects of the input sentence. It is also learning a
continuous (rather than categorical) representation of the style of the
sentence, which is more linguistically realistic. The model uses
adversarial-motivational training and includes a special motivational loss,
which acts opposite to the discriminator and encourages a better decomposition.
Furthermore, we evaluate the obtained meaning embeddings on a downstream task
of paraphrase detection and show that they significantly outperform the
embeddings of a regular autoencoder.

本文提出了一种文本表示的对抗性分解方法，并通过两个案例展示其能够进行精细控制元素变化，并学习连续的风格表示。该模型使用对抗性训练和特定的动机损失，并在下游任务中表现优异。