Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

Stephan Oepen, Omri Abend, Jan Hajic, Daniel Hershcovich, Marco Kuhlmann, Tim O’Gorman, Nianwen Xue (Editors)


Anthology ID:
K19-2
Month:
November
Year:
2019
Address:
Hong Kong
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/K19-2
DOI:
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PDF:
https://aclanthology.org/K19-2.pdf

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Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning
Stephan Oepen | Omri Abend | Jan Hajic | Daniel Hershcovich | Marco Kuhlmann | Tim O’Gorman | Nianwen Xue

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MRP 2019 : Cross-Framework Meaning Representation ParsingMRP 2019: Cross-Framework Meaning Representation Parsing
Stephan Oepen | Omri Abend | Jan Hajic | Daniel Hershcovich | Marco Kuhlmann | Tim O’Gorman | Nianwen Xue | Jayeol Chun | Milan Straka | Zdenka Uresova

The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the representation of sentence meaning in the form of directed graph were represented in the training and evaluation data for the task, packaged in a uniform abstract graph representation and serialization. The task received submissions from eighteen teams, of which five do not participate in the official ranking because they arrived after the closing deadline, made use of additional training data, or involved one of the task co-organizers. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at : http://mrp.nlpl.eu

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The ERG at MRP 2019 : Radically Compositional Semantic DependenciesERG at MRP 2019: Radically Compositional Semantic Dependencies
Stephan Oepen | Dan Flickinger

The English Resource Grammar (ERG) is a broad-coverage computational grammar of English that outputs underspecified logical-form representations of meaning in a framework dubbed English Resource Semantics (ERS). Two of the target representations in the the 2019 Shared Task on Cross-Framework Meaning Representation Parsing (MRP 2019) derive graph-based simplifications of ERS, viz. Elementary Dependency Structures (EDS) and DELPH-IN MRS Bi-Lexical Dependencies (DM). As a point of reference outside the official MRP competition, we parsed the evaluation strings using the ERG and converted the resulting meaning representations to EDS and DM. These graphs yield higher evaluation scores than the purely data-driven parsers in the actual shared task, suggesting that the general-purpose linguistic knowledge about English grammar encoded in the ERG can add value when parsing into these meaning representations.

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SJTU-NICT at MRP 2019 : Multi-Task Learning for End-to-End Uniform Semantic Graph ParsingSJTU-NICT at MRP 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing
Zuchao Li | Hai Zhao | Zhuosheng Zhang | Rui Wang | Masao Utiyama | Eiichiro Sumita

This paper describes our SJTU-NICT’s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Our system uses a graph-based approach to model a variety of semantic graph parsing tasks. Our main contributions in the submitted system are summarized as follows : 1. Our model is fully end-to-end and is capable of being trained only on the given training set which does not rely on any other extra training source including the companion data provided by the organizer ; 2. We extend our graph pruning algorithm to a variety of semantic graphs, solving the problem of excessive semantic graph search space ; 3. We introduce multi-task learning for multiple objectives within the same framework. The evaluation results show that our system achieved second place in the overall F_1 score and achieved the best F_1 score on the DM framework.F_1 score and achieved the best F_1 score on the DM framework.

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CUHK at MRP 2019 : Transition-Based Parser with Cross-Framework Variable-Arity Resolve ActionCUHK at MRP 2019: Transition-Based Parser with Cross-Framework Variable-Arity Resolve Action
Sunny Lai | Chun Hei Lo | Kwong Sak Leung | Yee Leung

This paper describes our system (RESOLVER) submitted to the CoNLL 2019 shared task on Cross-Framework Meaning Representation Parsing (MRP). Our system implements a transition-based parser with a directed acyclic graph (DAG) to tree preprocessor and a novel cross-framework variable-arity resolve action that generalizes over five different representations. Although we ranked low in the competition, we have shown the current limitations and potentials of including variable-arity action in MRP and concluded with directions for improvements in the future.

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Hitachi at MRP 2019 : Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation ParsingMRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing
Yuta Koreeda | Gaku Morio | Terufumi Morishita | Hiroaki Ozaki | Kohsuke Yanai

This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating systems were asked to predict nodes, edges and their attributes for five frameworks, each with different order of abstraction from input tokens. We proposed a unified encoder-to-biaffine network for all five frameworks, which effectively incorporates a shared encoder to extract rich input features, decoder networks to generate anchorless nodes in UCCA and AMR, and biaffine networks to predict edges. Our system was ranked fifth with the macro-averaged MRP F1 score of 0.7604, and outperformed the baseline unified transition-based MRP. Furthermore, post-evaluation experiments showed that we can boost the performance of the proposed system by incorporating multi-task learning, whereas the baseline could not. These imply efficacy of incorporating the biaffine network to the shared architecture for MRP and that learning heterogeneous meaning representations at once can boost the system performance.

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FAL-Oslo at MRP 2019 : Garage Sale Semantic ParsingÚFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing
Kira Droganova | Andrey Kutuzov | Nikita Mediankin | Daniel Zeman

This paper describes the FALOslo system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP, Oepen et al. The submission is based on several third-party parsers. Within the official shared task results, the submission ranked 11th out of 13 participating systems.

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Peking at MRP 2019 : Factorization- and Composition-Based Parsing for Elementary Dependency StructuresMRP 2019: Factorization- and Composition-Based Parsing for Elementary Dependency Structures
Yufei Chen | Yajie Ye | Weiwei Sun

We design, implement and evaluate two semantic parsers, which represent factorization- and composition-based approaches respectively, for Elementary Dependency Structures (EDS) at the CoNLL 2019 Shared Task on Cross-Framework Meaning Representation Parsing. The detailed evaluation of the two parsers gives us a new perception about parsing into linguistically enriched meaning representations : current neural EDS parsers are able to reach an accuracy at the inter-annotator agreement level in the same-epoch-and-domain setup.