Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics
This work addresses the need for robust natural language understanding by systematically analyzing complementarity between frameworks, though it is incremental in nature.
The paper tackled the problem of comparing linguistic meaning representations by mapping STREUSLE annotations to UCCA, finding that both rule-based and supervised methods achieved high accuracy close to fully supervised parsers, indicating partial redundancy.
Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality---indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.