Synonymy and translational equivalence are the relations of sameness of
meaning within and across languages. As the principal relations in wordnets and
multi-wordnets, they are vital to computational lexical semantics, yet the
field suffers from the absence of a common formal framework to define their
properties and relationship. This paper proposes a unifying treatment of these
two relations, which is validated by experiments on existing resources. In our
view, synonymy and translational equivalence are simply different types of
semantic identity. The theory establishes a solid foundation for critically
re-evaluating prior work in cross-lingual semantics, and facilitating the
creation, verification, and amelioration of lexical resources.

本文提出了一种统一处理同义词和翻译等效性这两种语言关系的方法，即将它们视为不同类型的语义同一性，并通过对现有资源的实验进行验证，为跨语言语义学中的先前工作重新评估和词汇资源的创建、验证和改善建立了一个坚实的基础。

同义词 = 翻译等价

Synonymy = Translational Equivalence

Parallel sentence extraction is a task addressing the data sparsity problem
found in multilingual natural language processing applications. We propose an
end-to-end deep neural network approach to detect translational equivalence
between sentences in two different languages. In contrast to previous
approaches, which typically rely on multiples models and various word alignment
features, by leveraging continuous vector representation of sentences we remove
the need of any domain specific feature engineering. Using a siamese
bidirectional recurrent neural networks, our results against a strong baseline
based on a state-of-the-art parallel sentence extraction system show a
significant improvement in both the quality of the extracted parallel sentences
and the translation performance of statistical machine translation systems. We
believe this study is the first one to investigate deep learning for the
parallel sentence extraction task.

本文提出了一种基于深度神经网络的端到端方法，用于检测两种不同语言之间句子之间的翻译等效性，结果表明该方法在提取平行句子的质量和统计机器翻译系统的翻译表现上均有显著改善，为平行句子提取任务的深度学习研究提供了一种新的思路。