TL;DR
This paper describes a novel, framework-agnostic neural network system for semantic parsing across multiple meaning representation frameworks, achieving third place in the CoNLL 2019 shared task.
Contribution
It introduces a uniform graph-to-graph neural network architecture that operates without prior knowledge of graph structures, extending UDPipe 2.0 for semantic parsing.
Findings
Achieved third place in MRP 2019 shared task
Proposed a framework-agnostic neural architecture
Demonstrated effective implicit modeling of meaning graphs
Abstract
We present a system description of our contribution to the CoNLL 2019 shared task, Cross-Framework Meaning Representation Parsing (MRP 2019). The proposed architecture is our first attempt towards a semantic parsing extension of the UDPipe 2.0, a lemmatization, POS tagging and dependency parsing pipeline. For the MRP 2019, which features five formally and linguistically different approaches to meaning representation (DM, PSD, EDS, UCCA and AMR), we propose a uniform, language and framework agnostic graph-to-graph neural network architecture. Without any knowledge about the graph structure, and specifically without any linguistically or framework motivated features, our system implicitly models the meaning representation graphs. After fixing a human error (we used earlier incorrect version of provided test set analyses), our submission would score third in the competition evaluation.…
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