Simple and Effective Graph-to-Graph Annotation Conversion (2022)

Abstract: Annotation conversion is an effective way to construct datasets under new annotation guidelines based on existing datasets with little human labour. Previous work has been limited in conversion between tree-structured datasets and mainly focused on feature-based models which are not easily applicable to new conversions. In this paper, we propose two simple and effective graph-to-graph annotation conversion approaches, namely Label Switching and Graph2Graph Linear Transformation, which use pseudo data and inherit parameters to guide graph conversions respectively. These methods are able to deal with conversion between graph-structured annotations and require no manually designed features. To verify their effectiveness, we manually construct a graph-structured parallel annotated dataset and evaluate the proposed approaches on it as well as other existing parallel annotated datasets. Experimental results show that the proposed approaches outperform strong baselines with higher conversion score. To further validate the quality of converted graphs, we utilize them to train the target parser and find graphs generated by our approaches lead to higher parsing score than those generated by the baselines.

## introduction while tree-structured representations have dominated parsing for the last decade, graph-structured datasets are receiving growing interest in recent years @xcite . over the last few years, an increasing number of graph-structured datasets have become available. some of them, such as dm corpora from the semeval 2015 task 18 dataset @xcite and amrbank @xcite , are manually annotated. some * equal contribution. ## method in this section, we first give a formal definition of the task of supervised graph-to-graph annotation conversion (section 3.1). then, we present the proposed approaches, namely label switching (section 3.2) and graph2graph linear transformation (section 3.3) for this task. ## conclusion this paper aims at graph-structured annotation conversion based on an existing source-annotated dataset with little human labour. we propose two graph-to-graph annotation conversion approaches, namely label switching and graph2graph linear transformation, and show their effectiveness on eight annotation conversion tasks and converted data utilizing tasks. results show that 1) the two approaches achieve comparable conversion scores; 2) our proposed approaches are most suitable for cases where only limited parallel annotated data is available; 3) the two approaches can be combined to further improve the performance.