Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, full-document parsing into a unified graph representation lacks well-defined representation and evaluation. Taking advantage of a super-sentential level of AMR coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under-merging. Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs, and use it to re-evaluate the best published document-level AMR parser. We also present a pipeline approach combining the top performing AMR parser and coreference resolution systems, providing a strong baseline for future research.
Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
Kevin Gu, Eva Tuecke, et al.
ICML 2024
Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021
Daiki Kimura, Tsunehiko Tanaka, et al.
NAACL 2022