CUHK at MRP 2019: Transition-Based Parser with Cross-Framework Variable-Arity Resolve Action (original) (raw)

MRP 2019: Cross-Framework Meaning Representation Parsing

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

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 graphs were represented in the training and evaluation data for the task, packaged in a uniform graph abstraction 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 extra training data, or involved one of the task co-organizers. All technical information regarding the task, including system submissions, official results,

MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing

Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing, 2020

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. 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:

An Incremental Parser for Abstract Meaning Representation

2016

Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.

Challenges in mapping of syntactic representations for framework-independent parser evaluation

2008

We explore some of the issues and challenges created by the incompatibility of diverse representation schemes for syntactic parsing. In particular, we examine the problem of output format conversion for evaluation of parsers that use different formalisms. We discuss recent related efforts, and present an evaluation of different parsers that use representations that vary not only in formalisms, but also in depth of syntactic information. We attempt to compare these parsers in a domain widely used for parser evaluation, the Wall Street Journal section of the Penn Treebank, and in the academic biomedical literature, where the use of parsing technologies is expected to contribute in practical applications, such as information extraction and text mining.

The best of both worlds: a graph-based completion model for transition-based parsers

Proceedings of the 13th Conference of the European Chapter of the Association For Computational Linguistics, 2012

Transition-based dependency parsers are often forced to make attachment decisions at a point when only partial information about the relevant graph configuration is available. In this paper, we describe a model that takes into account complete structures as they become available to rescore the elements of a beam, combining the advantages of transition-based and graph-based approaches. We also propose an efficient implementation that allows for the use of sophisticated features and show that the completion model leads to a substantial increase in accuracy. We apply the new transition-based parser on typologically different languages such as English, Chinese, Czech, and German and report competitive labeled and unlabeled attachment scores.

Comparing advanced graph-based and transition-based dependency parsers

In this paper, we compare a higher order graph-based parser and a transitionbased parser with beam search. These parsers provide a higher accuracy than a second order MST parser and a deterministic transition-based parser. We apply and compare the output on languages, which have not been in the research focus of Shared Tasks. The parser are implemented in a uniform framework. The transitionbased parser was newley implemented and we revised the graph-based parser. The graph-based parser has to our knowlege the highest published scores for French and Czech with 90.40 and 81.43 labeled accuracy score.

In-House: An Ensemble of Pre-Existing Off-the-Shelf Parsers

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014

This submission to the open track of Task 8 at SemEval 2014 seeks to connect the Task to pre-existing, 'in-house' parsing systems for the same types of target semantic dependency graphs.