Abstract Meaning Representation (AMR) is a graph-based semantic representation which captures the core elements of meaning of a text. AMR has been incorporated into a variety of downstream tasks, which rely heavily on the availability of gold-annotated AMR corpora. While the annotation process is fairly lightweight, annotator training is still required even for linguists due to the extensive nature of the annotation guidelines and comprehensive set of roles. Therefore, all corpus development projects for AMR (and extensions of AMR) require the dataset curators to first train annotators. In this paper, we develop an online AMR annotation training system called TrAinMR in order to ease this training process and thus motivate the development of additional AMR corpora. The two main components of TrAinMR are (1) a written tutorial covering the basics of AMR annotation, and (2) an interactive practice module with corrective feedback. To measure the effectiveness of this tool, we conduct two pilot studies with five human annotators each. We find that the majority of annotators state their understanding of AMR improved as a result of TrAinMR, and some annotators show a positive trend in SMATCH scores after completing the practice module.

The graph-based semantic representation Abstract Meaning Representation (AMR) incorporates Proposition Bank (PropBank) sense IDs to indicate the senses of nodes in the graph and specify their associated arguments. While this contributes to the semantic information captured in an AMR graph, the utility of incorporating sense IDs into AMR graphs has not been analyzed from a technological perspective, i.e. how useful sense IDs are to generating text from AMRs and how accurately senses are induced by AMR parsers. In this work, we examine the effects of altering or removing the sense IDs in the AMR graphs, by perturbing the sense data passed to AMR-to-text generation models. Additionally, for text-to-AMR parsing, we quantitatively and qualitatively verify the accuracy of sense IDs produced from state-of-the-art models. Our investigation reveals that sense IDs do contribute a small amount to accurate AMR-to-text generation, meaning they enhance AMR technologies, but may be disregarded when their reliance prohibits multilingual corpus development.