HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser

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Abstract

This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing. The task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG. Our solution consists of two subsystems: transition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG) and iterative inference parser for Flavor (2) frameworks (DRG, AMR). In the final evaluation, our system is ranked 3rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.

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Dou, L., Feng, Y., Ji, Y., Che, W., & Liu, T. (2020). HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser. In CoNLL 2020 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing (pp. 65–72). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.conll-shared.6

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