HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser
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 sub-systems: 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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
HUJI-KU at MRP 2020: Two Transition-based Neural Parsers
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-…
Vocal Bursts Valence PredictionHUJI-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…
Semantic ParsingVocal Bursts Valence PredictionHIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding
This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing. We extended the basic transition-based parser with two improvements: a) Efficient Training by realizi…
UCCA ParsingWord EmbeddingsImproving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers
We generalize Cohen, G\'omez-Rodr\'iguez, and Satta's (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference. This includes novel parsers with better cove…
Dependency Grammar Induction with a Neural Variational Transition-based Parser
Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-of-the-art results on this task but they require …
Dependency Grammar InductionVariational Inference