Widely Interpretable Semantic Representation: Frameless Meaning
  Representation for Broader Applicability
Lydia Feng Gregor Williamson Han He Jinho D. Choi
This paper presents a novel semantic representation, WISeR, that overcomes
challenges for Abstract Meaning Representation (AMR). Despite its strengths,
AMR is not easily applied to languages or domains without predefined semantic
frames, and its use of numbered arguments results in semantic role labels,
which are not directly interpretable and are semantically overloaded for
parsers. We examine the numbered arguments of predicates in AMR and convert
them to thematic roles that do not require reference to semantic frames. We
create a new corpus of 1K English dialogue sentences annotated in both WISeR
and AMR. WISeR shows stronger inter-annotator agreement for beginner and
experienced annotators, with beginners becoming proficient in WISeR annotation
more quickly. Finally, we train a state-of-the-art parser on the AMR 3.0 corpus
and a WISeR corpus converted from AMR 3.0. The parser is evaluated on these
corpora and our dialogue corpus. The WISeR model exhibits higher accuracy than
its AMR counterpart across the board, demonstrating that WISeR is easier for
parsers to learn.