论文标题
用抽象的含义表示测量细粒的语义等效性
Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation
论文作者
论文摘要
识别语义上等效的句子对于许多跨语义和单语语言NLP任务很重要。当前的语义等效方法采用了“等效”的宽松，句子级别的方法，尽管以前的证据表明细粒度的差异和隐性内容对人类的理解有影响（Roth and Anthonio，2021年）和系统性能（Briakou and Carpuat，2021年）。在这项工作中，我们介绍了一种表征语义等效性的新颖，更敏感的方法，该方法利用抽象含义表示结构。我们开发一种方法，可以与黄金或自动AMR注释一起使用，并证明我们的解决方案实际上比现有的语料库过滤方法更细粒，并且比现有的语义相似性指标更准确地预测严格的等效句子。我们建议，我们的语义等效性量度更细的度量可能会限制人类后的机器翻译任务和人类对句子相似性的评估。
Identifying semantically equivalent sentences is important for many cross-lingual and mono-lingual NLP tasks. Current approaches to semantic equivalence take a loose, sentence-level approach to "equivalence," despite previous evidence that fine-grained differences and implicit content have an effect on human understanding (Roth and Anthonio, 2021) and system performance (Briakou and Carpuat, 2021). In this work, we introduce a novel, more sensitive method of characterizing semantic equivalence that leverages Abstract Meaning Representation graph structures. We develop an approach, which can be used with either gold or automatic AMR annotations, and demonstrate that our solution is in fact finer-grained than existing corpus filtering methods and more accurate at predicting strictly equivalent sentences than existing semantic similarity metrics. We suggest that our finer-grained measure of semantic equivalence could limit the workload in the task of human post-edited machine translation and in human evaluation of sentence similarity.
