This paper presents contributions in two directions: first we propose a new system for Frame Identification (FI), based on pre-trained text encoders trained discriminatively and graphs embedding, producing state of the art performance and, second, we take in consideration all the extremely different procedures used to evaluate systems for this task performing a complete evaluation over two benchmarks and all possible splits and cleaning procedures used in the FI literature.

Combining ELECTRA and Adaptive Graph Encoding for Frame Identification

Tamburini Fabio
2022

Abstract

This paper presents contributions in two directions: first we propose a new system for Frame Identification (FI), based on pre-trained text encoders trained discriminatively and graphs embedding, producing state of the art performance and, second, we take in consideration all the extremely different procedures used to evaluate systems for this task performing a complete evaluation over two benchmarks and all possible splits and cleaning procedures used in the FI literature.
2022
Proceedings of the Thirteenth Language Resources and Evaluation Conference
1671
1679
Tamburini Fabio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/897476
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