Abstract
This paper describes the HUJI-KU system submission to the shared task on CrossFramework 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 crossframework and cross-lingual tracks.- Anthology ID:
- 2020.conll-shared.7
- Volume:
- Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 73–82
- Language:
- URL:
- https://aclanthology.org/2020.conll-shared.7
- DOI:
- 10.18653/v1/2020.conll-shared.7
- Cite (ACL):
- Ofir Arviv, Ruixiang Cui, and Daniel Hershcovich. 2020. HUJI-KU at MRP 2020: Two Transition-based Neural Parsers. In Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing, pages 73–82, Online. Association for Computational Linguistics.
- Cite (Informal):
- HUJI-KU at MRP 2020: Two Transition-based Neural Parsers (Arviv et al., CoNLL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.conll-shared.7.pdf