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5.2 Baseline Modelsīefore conducting a large number of experiments, we investigate how many iterations of refinement are useful, given the computational costs of additional iterations. ( 2018), we keep punctuation for evaluation on the UD Treebanks and the German corpus and remove it for the Penn Treebanks (Nilsson and Nivre, 2008). For our evaluation on the German Treebank of the CoNLL 2009 shared task (Hajič et al., 2009), we apply the same setup as defined in Kuncoro et al. For Chinese, we apply the same setup as described in Chen and Manning ( 2014), including the use of gold PoS tags. For English, we use the same setting as defined in Mohammadshahi and Henderson ( 2020). For our evaluation of Penn Treebanks, we use the English and Chinese Penn Treebanks (Marcus et al., 1993 Xue et al., 2002). This set contains several languages with different language families, scripts, character set sizes, morphological complexity, and training sizes and domains. For our evaluation on UD Treebanks (UD v2.3) (Nivre et al., 2018), we select languages based on the criteria proposed in de Lhoneux et al. To evaluate our models, we apply them on several kinds of datasets, namely, Universal Dependency (UD) Treebanks, Penn Treebanks, and the German CoNLL 2009 Treebank. Typically, neural graph-based models consist of two components: an encoder that learns context-dependent vector representations for the nodes of the dependency graph, and a decoder that computes the dependency scores for each pair of nodes and then applies a decoding algorithm to find the highest-scoring dependency tree. Graph-based parsers (Eisner, 1996 McDonald et al., 2005a Koo and Collins, 2010) compute scores for every possible dependency edge and then apply a decoding algorithm to find the highest scoring total tree. We take a graph-based approach to this correction. As in our approach, transformation-based (Satta and Brill, 1996) and correctivemodeling parsers use various methods (e.g., Knight and Graehl, 2005 Hall and Novák, 2005 Attardi and Ciaramita, 2007 Hennig and Köhn, 2017 Zheng, 2017) to correct an initial parse. Transition-based parsers predict the dependency graph one edge at a time through a sequence of parsing actions (Yamada and Matsumoto, 2003 Nivre and Scholz, 2004 Titov and Henderson, 2007 Zhang andNivre, 2011). There are several approaches to compute the dependency tree. Syntactic dependency parsing is a critical component in a variety of natural language understanding tasks, such as semantic role labeling (Marcheggiani and Titov, 2017), machine translation (Chen et al., 2017), relation extraction (Zhanget al., 2018), and natural language interfaces (Pang et al., 2019).