Evaluating Scoped Meaning Representations

TL;DR

This paper introduces a multilingual, semantically annotated corpus with a novel evaluation method for scoped meaning representations, enabling improved semantic parser assessment and translation analysis.

Contribution

It presents a new multilingual corpus with scoped meaning representations and a matching evaluation tool for semantic parser performance and translation quality assessment.

Findings

Matching tool is accurate and efficient

Semantic parsers achieved F-scores between 43% and 54%

Automatic translation comparison helps identify annotation errors and semantic gaps

Abstract

Semantic parsing offers many opportunities to improve natural language understanding. We present a semantically annotated parallel corpus for English, German, Italian, and Dutch where sentences are aligned with scoped meaning representations in order to capture the semantics of negation, modals, quantification, and presupposition triggers. The semantic formalism is based on Discourse Representation Theory, but concepts are represented by WordNet synsets and thematic roles by VerbNet relations. Translating scoped meaning representations to sets of clauses enables us to compare them for the purpose of semantic parser evaluation and checking translations. This is done by computing precision and recall on matching clauses, in a similar way as is done for Abstract Meaning Representations. We show that our matching tool for evaluating scoped meaning representations is both accurate and efficient. Applying this matching tool to three baseline semantic parsers yields F-scores between 43% and 54%. A pilot study is performed to automatically find changes in meaning by comparing meaning representations of translations. This comparison turns out to be an additional way of (i) finding annotation mistakes and (ii) finding instances where our semantic analysis needs to be improved.