论文标题
翻新具有抽象含义表示的多语言句子嵌入
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
论文作者
论文摘要
我们介绍了一种新方法，以改善具有抽象含义表示（AMR）的现有多语言句子嵌入。与原始文本输入相比，AMR是一种结构化的语义表示，它明确，明确地呈现句子中的核心概念和关系。它还有助于减少不同表达式和语言的表面变化。与大多数仅评估衡量语义相似性能力的工作不同，我们对现有的多语言句子嵌入和我们的改进版本进行了彻底评估，其中包括在不同下游应用程序中的五项转移任务的集合。实验结果表明，使用AMR进行多种语言式嵌入的翻新会导致语义文本相似性和传输任务的最先进性能。可以在\ url {https://github.com/jcyk/mse-amr}找到我们的代码库和评估脚本。
We introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR). Compared with the original textual input, AMR is a structured semantic representation that presents the core concepts and relations in a sentence explicitly and unambiguously. It also helps reduce surface variations across different expressions and languages. Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications. Experiment results show that retrofitting multilingual sentence embeddings with AMR leads to better state-of-the-art performance on both semantic textual similarity and transfer tasks. Our codebase and evaluation scripts can be found at \url{https://github.com/jcyk/MSE-AMR}.
