HUJI-KU at MRP~2020: Two Transition-based Neural Parsers

HUJI-KU at MRP~2020: Two Transition-based Neural Parsers

This paper describes the HUJI-KU system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system i...

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Veröffentlicht in:arXiv.org 2020-10
Hauptverfasser: Arviv, Ofir, Cui, Ruixiang, Hershcovich, Daniel
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Learning
Material requirements planning
Parsers
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description This paper describes the HUJI-KU system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the cross-framework and cross-lingual tracks.
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Material requirements planning
Parsers
title HUJI-KU at MRP~2020: Two Transition-based Neural Parsers
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