Hitachi at MRP 2020: Text-to-Graph-Notation Transducer (2020)

Abstract: This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages. We propose to unify these tasks as a text-to-graph-notation transduction in which we convert an input text into a graph notation. To this end, we designed a novel Plain Graph Notation (PGN) that handles various graphs universally. Then, our parser predicts a PGN-based sequence by leveraging Transformers and biaffine attentions. Notably, our parser can handle any PGN-formatted graphs with fewer framework-specific modifications. As a result, ensemble versions of the parser tied for 1st place in both cross-framework and cross-lingual tracks.

## introduction this paper introduces the proposed parser of the hitachi team for the conll 2020 cross-framework meaning representation parsing (mrp 2020) shared task @xcite . different from the previous mrp 2019 shared task @xcite , there are two tracks. the first is a cross-framework track that aims at parsing english sentences to five different meaning representation graphs, i.e., eds @xcite , ptg @xcite , ucca @xcite , amr @xcite @xcite . the other is a cross-lingual track that targets four different frameworks and three languages, i.e., german ucca and drg, chinese amr @xcite , and czech ptg. in both tracks, the goal was to design a unified parser for all graphs. figure 1 : given an input text, we tokenize and encode the text by pre-trained transformer encoder (e.g., bert). then, transformer decoder is applied to produce a plain graph notation (pgn) that is convertible into a general graph. in this paper, we propose a novel parser to unify graph predictions across all frameworks and languages. to this end, we introduce a text-to-graphnotation transduction. the overview of our parser is shown in figure 1 . our parser utilizes sequenceto-sequence transformer architectures @xcite to generate a plain graph notation (pgn), which we newly designed as a contextfree language. pgn is a simplified notation based on the penman notation @xcite ) that is generally used for amr graphs. however, pgn is tailored for direct generation by sequence-to-sequence architecture. our parser is expected to combine both strengths of the neural graph-based and transition-based parsers @xcite @xcite . this is because the transformer decoder directly draws attentions like a graph-based parser does, as well as handles higher-level effects of graph structures by sequence prediction like a transition-based parser does. moreover, our parser is practically able to parse most of the graph vari-ants in a unified manner. for example, our parser is able to predict directed acyclic graphs, disconnected graphs, directed multigraphs, reentrancy edges @xcite , and source-side anchors without complicated languagedependent architectures. consequently, ensemble versions of our parser officially tied for 1st place in both the crossframework track and cross-lingual track, achieving the top performances for english eds, ptg, and amr graphs. we also summarize other contributions as follows: alignment free: pgn generation allows us to achieve completely alignment-free parsing. action design free: compared to a transitionbased parser, there is no need to design a complex transition strategy. fast training: since we leverage attentions, train speed is faster than a transition-based parser. ## plain graph notation 3.1 format design to represent a graph as a text sequence, we newly designed a notation, called plain graph notation (pgn), with the key principles shown below. simpler format: similar to penman notation @xcite , which is used to represent amr graphs with text sequence, pgn is based on a context-free grammar. tree-like structure: we employed an essentially tree-like structure 2 because all spanning graphs with a root can be converted into tree-like structures by flipping the directions of appropriate edges. this structure is useful when we convert graphs to pgn. ## conclusion this paper described a novel parser for the shared task on meaning representation parsing 2020. we proposed a text-to-graph-notation transduction that provides a novel graph notation. our model effectively parsed the graph-notation. experimental results showed that our parser achieved the top performances in many frameworks. since our parser is not limited to the five frameworks, in future work we will extend our technique for other tasks.