Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation.

CLLRD@LREC(2020)

引用 0|浏览2
暂无评分
摘要
Abstract Meaning Representations (AMRs), a syntax-free representation of phrase semantics are useful for capturing the meaning of a phrase and reflecting the relationship between concepts that are referred to. However, annotating AMRs are time consuming and expensive. The existing annotation process requires expertly trained workers who have knowledge of an extensive set of guidelines for parsing phrases. In this paper, we propose a cost-saving two-step process for the creation of a corpus of AMR-phrase pairs for spatial referring expressions. The first step uses non-specialists to perform simple annotations that can be leveraged in the second step to accelerate the annotation performed by the experts. We hypothesize that our process will decrease the cost per annotation and improve consistency across annotators. Few corpora of spatial referring expressions exist and the resulting language resource will be valuable for referring expression comprehension and generation modeling.
更多
查看译文
关键词
time efficient amr annotation,non-specialists
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要