JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference (2020)

Abstract: This paper describes the Jeonbuk National University (JBNU) system for the 2020 shared task on Cross-Framework Meaning Representation Parsing at the Conference on Computational Natural Language Learning. Among the five frameworks, we address only the abstract meaning representation framework and propose a joint state model for the graph-sequence iterative inference of (Cai and Lam, 2020) for a simplified graph-sequence inference. In our joint state model, we update only a single joint state vector during the graph-sequence inference process instead of keeping the dual state vectors, and all other components are exactly the same as in (Cai and Lam, 2020).

## introduction recent studies on meaning representation parsing (mrp) have focused on different semantic graph frameworks @xcite such as bilexical semantic dependency graphs @xcite @xcite @xcite , a universal conceptual cognitive annotation @xcite , abstract meaning representation @xcite @xcite @xcite @xcite , and a discourse representation structure @xcite @xcite @xcite . to jointly address various semantic graphs, the aim of the cross-framework mrp task at the 2020 conference (mrp 2020) on computational natural language learning (conll) is to develop semantic graph parsing across the following five frameworks @xcite ): 1) eds: elementary dependency structures @xcite , 2) ptg: prague tectogrammatical graphs @xcite , 3) ucca: universal conceptual cognitive annotation @xcite , 4) amr: abstract meaning representation @xcite , and 5) drg: discourse representation graphs @xcite . for mrp 2020, we address only the amr framework and present a joint state model for graphsequence iterative inference, as a simple extension of @xcite . the graph-sequence iterative model of @xcite incrementally constructs an amr graph starting from an empty graph g 0 by alternatively applying two modules: 1) concept solver, which uses a previous graph hypothesis g i to predict a new concept, and 2) relation solver, which uses a previous concept hypothesis to predict relations for the new concept. the dual-state model of @xcite deploys two state vectors x t and y t for the graphsequence iterative inference, which refers to the t-th sequence hypothesis and t-th graph hypothesis, respectively. unlike the dual state model, we instead maintain a joint state vector z t , which encodes both sequence and graph hypotheses to apply a graph-sequence iterative inference in a simple and unified manner. during the iterative inference stage, we take the current joint state vector as a query vector and update the next joint state vector by applying attention mechanisms both to the text (i.e., sequence memory) and graph (i.e., graph memory) parts separately. the final joint state vector is then passed to the concept and relation solvers, which predict new concepts and their relations, respectively, as with the dual state model by @xcite . we submitted the results of our amr parsing model during the post-evaluation stage and ranked between 3rd and 4th place among the participants in the official results under the cross-framework metric foot_0 . the remainder of this paper is organized as follows: section 2 presents the detailed architecture of our system. section 3 describes the detailed process used for training biaffine attention models. section 4 provides the official results of mrp 2020. finally, some concluding remarks and a description of future research are given in section 5. ## model figure 1 shows the neural architecture based on the joint state model for a graph-sequence iterative inference. the neural architecture consists of five components: a 1) sequence encoder, 2) graph encoder, 3) concept solver, 4) relation solver, and 5) joint state model for a graph-sequence iterative inference. in the following, we briefly summarize the first four components of our model, which are almost the same as those of @xcite , where only the joint state vector is used as the unified query vector for the concept and relation solvers. we then present our joint state model in more details.