Quantifying the degree of similarity between images is a key copyright issue
for image-based machine learning. In legal doctrine however, determining the
degree of similarity between works requires subjective analysis, and
fact-finders (judges and juries) can demonstrate considerable variability in
these subjective judgement calls. Images that are structurally similar can be
deemed dissimilar, whereas images of completely different scenes can be deemed
similar enough to support a claim of copying. We seek to define and compute a
notion of "conceptual similarity" among images that captures high-level
relations even among images that do not share repeated elements or visually
similar components. The idea is to use a base multi-modal model to generate
"explanations" (captions) of visual data at increasing levels of complexity.
Then, similarity can be measured by the length of the caption needed to
discriminate between the two images: Two highly dissimilar images can be
discriminated early in their description, whereas conceptually dissimilar ones
will need more detail to be distinguished. We operationalize this definition
and show that it correlates with subjective (averaged human evaluation)
assessment, and beats existing baselines on both image-to-image and
text-to-text similarity benchmarks. Beyond just providing a number, our method
also offers interpretability by pointing to the specific level of granularity
of the description where the source data are differentiated.

图像相似度的量化是图像机器学习的一个关键版权问题。本文提出了一种基于概念相似性的图像相似度度量方法，通过生成逐渐复杂的图像描述来衡量相似度，该方法在图像对比和文本对比中表现优于现有基准，并通过指示差异描述粒度提供了解释性。

通过复杂性约束的描述性自编码实现可解释的概念相似度度量

Interpretable Measures of Conceptual Similarity by  Complexity-Constrained Descriptive Auto-Encoding

An interesting line of research in natural language processing (NLP) aims to
incorporate linguistic typology to bridge linguistic diversity and assist the
research of low-resource languages. While most works construct linguistic
similarity measures based on lexical or typological features, such as word
order and verbal inflection, recent work has introduced a novel approach to
defining language similarity based on how they represent basic concepts, which
is complementary to existing similarity measures. In this work, we study the
conceptual similarity in detail and evaluate it extensively on a binary
classification task.

本研究旨在将语言学的特性纳入到自然语言处理（NLP）中，通过基于基本概念的表示来定义语言之间的相似性并将其应用于二元分类任务中的低资源语言研究。

概念语言相似性的研究：比较与评估

A study of conceptual language similarity: comparison and evaluation

Neural language models learn word representations, or embeddings, that
capture rich linguistic and conceptual information. Here we investigate the
embeddings learned by neural machine translation models, a recently-developed
class of neural language model. We show that embeddings from translation models
outperform those learned by monolingual models at tasks that require knowledge
of both conceptual similarity and lexical-syntactic role. We further show that
these effects hold when translating from both English to French and English to
German, and argue that the desirable properties of translation embeddings
should emerge largely independently of the source and target languages.
Finally, we apply a new method for training neural translation models with very
large vocabularies, and show that this vocabulary expansion algorithm results
in minimal degradation of embedding quality. Our embedding spaces can be
queried in an online demo and downloaded from our web page. Overall, our
analyses indicate that translation-based embeddings should be used in
applications that require concepts to be organised according to similarity
and/or lexical function, while monolingual embeddings are better suited to
modelling (nonspecific) inter-word relatedness.

该研究探讨神经机器翻译模型所学到的嵌入，在需要同时考虑概念相似性和词汇 - 句法角色知识的任务中，它们的性能优于单语言模型所学到的嵌入。研究结果还表明，词汇扩展算法对嵌入质量的影响很小。

利用神经机器翻译嵌入词汇相似度

Embedding Word Similarity with Neural Machine Translation

Neural language models learn word representations that capture rich
linguistic and conceptual information. Here we investigate the embeddings
learned by neural machine translation models. We show that translation-based
embeddings outperform those learned by cutting-edge monolingual models at
single-language tasks requiring knowledge of conceptual similarity and/or
syntactic role. The findings suggest that, while monolingual models learn
information about how concepts are related, neural-translation models better
capture their true ontological status.

研究神经机器翻译模型和单语模型在表征词汇相似性和语法角色方面的性能，结果表明，神经翻译模型在捕捉概念的本体论状态方面比单语模型更胜一筹。