Treebank Embedding Vectors for Out-of-Domain Dependency Parsing

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8812–8818 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 8812 Treebank Embedding Vectors for Out-of-Domain Dependency Parsing Joachim Wagner and James Barry and Jennifer Foster ADAPT Centre School of Computing, Dublin City University, Ireland firstname.lastname@adaptcentre.ie Abstract A recent advance in monolingual dependency parsing is the idea of a treebank embedding vector, which allows all treebanks for a par- ticular language to be used as training data while at the same time allowing the model to prefer training data from one treebank over others and to select the preferred treebank at test time. We build on this idea by 1) intro- ducing a method to predict a treebank vector for sentences that do not come from a tree- bank used in training, and 2) exploring what happens when we move away from predefined treebank embedding vectors during test time and instead devise tailored interpolations. We show that 1) there are interpolated vectors that are superior to the predefined ones, and 2) tree- bank vectors can be predicted with sufficient accuracy, for nine out of ten test languages, to match the performance of an oracle approach that knows the most suitable predefined tree- bank embedding for the test set. 1 Introduction The Universal Dependencies project (Nivre et al., 2016) has made available multiple treebanks for the same language annotated according to the same scheme, leading to a new wave of research which explores ways to use multiple treebanks in mono- lingual parsing (Shi et al., 2017; Sato et al., 2017; Che et al., 2017; Stymne et al., 2018). Stymne et al. (2018) introduced a treebank em- bedding. A single model is trained on the concate- nation of the available treebanks for a language, and the input vector for each training token in- cludes the treebank embedding which encodes the treebank the token comes from. At test time, all input vectors in the test set of the same treebank are also assigned this treebank embedding vector. Stymne et al. (2018) show that this approach is superior to mono-treebank training and to plain treebank concatenation. Treebank embeddings per- form at about the same level as training on multiple treebanks and tuning on one, but they argue that a treebank embedding approach is preferable since it results in just one model per language. What happens, however, when the input sen- tence does not come from a treebank? Stymne et al. (2018) simulate this scenario with the Parallel Uni- versal Dependency (PUD) test sets. They define the notion of a proxy treebank which is the tree- bank to be used for a treebank embedding when parsing sentences that do not come from any of the training treebanks. They empirically determine the best proxy treebank for each PUD test set by testing with each treebank embedding. However, the question remains what to do with sentences for which no gold parse is available, and for which we do not know the best proxy. …

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