LAW-DMR 2021 - Williamson et al.


Intensionalizing Abstract Meaning Representations: Non-veridicality and Scope

Gregor Williamson, Patrick Elliott, Yuxin Ji, Jinho D. Choi*

*This author was mistakenly omitted for the workshop submission but included in the arXiv version.


Abstract

Abstract Meaning Representation (AMR) is a graphical meaning representation language designed to represent propositional information about argument structure. However, at present it is unable to satisfyingly represent non-veridical intensional contexts, often licensing inappropriate inferences. In this paper, we show how to resolve the problem of non-veridicality without appealing to layered graphs through a mapping from AMRs into Simply-Typed Lambda Calculus (STLC). At least for some cases, this requires the introduction of a new role :content which functions as an intensional operator. The translation proposed is inspired by the formal linguistics literature on the event semantics of attitude reports. Next, we address the interaction of quantifier scope and intensional operators in so-called de re/de dicto ambiguities. We adopt a scope node from the literature and provide an explicit multidimensional semantics utilizing Cooper storage which allows us to derive the de re and de dicto scope readings as well as intermediate scope readings which prove difficult for accounts without a scope node.

Venue / Year

Proceedings of the EMNLP Workshop on Linguistic Annotation (LAW) and Designing Meaning Representations (DMR) / 2021

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