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

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 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.

## introduction the goal of the conll 2020 shared task @xcite is to develop a unified parsing system to process all five semantic graph banks across different languages. this task combines five frameworks for graph-based meaning representation, each with its specific formal and linguistic assumptions, including ucca, eds, ptg, amr, and drg. 1 in the context of the shared task, the organizers distinguish different flavors of semantic graphs based on the nature of the relationship they assume between the linguistic surface string and the nodes of the graph. they call this relation anchoring. therefore, the involved five frameworks could be divided into two classes: (a) flavor (1), including ucca, eds, and ptg, allowing arbitrary parts of the sentence as node anchors, as well as multiple nodes anchored to overlapping sub-strings, and (b) flavor (2), including amr and drg, not considering the correspondence between nodes and the surface tokens. our submitted system could be summarized in the following: following @xcite , the top system in conll 2019 shared task @xcite , we employ the transition-based parser for flavor (1) frameworks since it's very flexible in predicting the anchor information. we directly use their parser for ucca and eds. and we design a new parser for ptg. ## background in the following, we will give a brief introduction to these frameworks and our corresponding solutions. universal conceptual cognitive annotation (ucca) is a multi-layer linguistic framework (flavor (1)) firstly proposed by @xcite , which treats input words as terminal nodes. the non-terminal node might govern one or more nodes, which may be discontinuous. moreover, one node can have multiple governing (parent) nodes through multiple edges, consisting of a single primary edge and other remote edges. relationships between nodes are represented by edge labels. the primary edges form a tree structure, whereas the remote edges introduce reentrancy, forming directed acyclic graphs (dags). we directly employ the system of @xcite , which achieves the 1 st at conll 2019 shared task. elementary dependency structure (eds) is a graph-structured semantic representation formalism (flavor (1)) proposed by @xcite . @xcite introduce a neural encoder-decoder transition-based parser for the eds graph, which extracts the node alignment (or anchoring) information effectively. prague tectogrammatical graphs (ptg) are graph-structured multi-layered semantic representation formalism (flavor (1)) proposed by @xcite . ptg graphs essentially recast core predicate-argument structure in the form of mostly anchored dependency graphs, albeit introducing 'empty' (or generated, in fgd terminology) nodes, for which there is no corresponding surface token. we didn't find any existing parser for ptg. thus we design a list-based arc-eager transitionbased parser for ptg. abstract meaning representation (amr), proposed by @xcite , is a broadcoverage sentence-level semantic formalism (flavor (2)) used to encode the meaning of natural language sentences. amr can be regarded as a rooted labeled directed acyclic graph. nodes in amr graphs represent concepts and labeled directed edges are relations between the concepts. we directly employ state-of-the-art parser of cai and lam (2020). discourse representation graphs (drg) are proposed by abzianidze et al. (2020) (flavor (2)), which are derived from the drs annotations in the parallel meaning bank @xcite . its concepts are represented by wordnet 3.0 @xcite senses and semantic roles by the adapted version of verbnet @xcite roles. similar to ptg, we don't find any existing parser for drg, thus we modify the amr parser to process the drg. ## pre-processing and post-processing in this session, we introduce the pre-processing and post-processing work. the mrp graph can be broken down into seven component pieces: top nodes, node labels, node properties, node anchoring, directed edges, edge labels, and edge attributes. the directed edges, edge labels, and node id form the standard input of our system. for node anchoring, we directly derive the anchoring information through segmentation from companion data. for other elements, such as top nodes, are a bit different among the frameworks. we will introduce these framework-specific work in the following. ## experiments in this section, we will show the basic model setup and overall evaluation results. ## conclusion in this paper, we describe our submission system for the conll 2020 shared task. we separate 8 evaluation results of conll 2020 shared task are available at http://bit.ly/cfmrp20 .