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This paper presents a novel semantic representation, WISeR, that overcomeschallenges for Abstract Meaning Representation (AMR). Despite its strengths,AMR is not easily applied to languages or domains without predefined semanticframes, and its use of numbered arguments results in semantic role labels,which are not directly interpretable and are semantically overloaded forparsers. We examine the numbered arguments of predicates in AMR and convertthem to thematic roles that do not require reference to semantic frames. Wecreate a new corpus of 1K English dialogue sentences annotated in both WISeRand AMR. WISeR shows stronger inter-annotator agreement for beginner andexperienced annotators, with beginners becoming proficient in WISeR annotationmore quickly. Finally, we train a state-of-the-art parser on the AMR 3.0 corpusand a WISeR corpus converted from AMR 3.0. The parser is evaluated on thesecorpora and our dialogue corpus. The WISeR model exhibits higher accuracy thanits AMR counterpart across the board, demonstrating that WISeR is easier forparsers to learn.

Widely Interpretable Semantic Representation: Frameless Meaning Representation for Broader Applicability