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Current methods of cross-lingual parser transfer focus on predicting the bestparser for a low-resource target language globally, that is, "at treebanklevel". In this work, we propose and argue for a novel cross-lingual transferparadigm: instance-level parser selection (ILPS), and present aproof-of-concept study focused on instance-level selection in the framework ofdelexicalized parser transfer. We start from an empirical observation thatdifferent source parsers are the best choice for different Universal POSsequences in the target language. We then propose to predict the best parser atthe instance level. To this end, we train a supervised regression model, basedon the Transformer architecture, to predict parser accuracies for individualPOS-sequences. We compare ILPS against two strong single-best parser selectionbaselines (SBPS): (1) a model that compares POS n-gram distributions betweenthe source and target languages (KL) and (2) a model that selects the sourcebased on the similarity between manually created language vectors encodingsyntactic properties of languages (L2V). The results from our extensiveevaluation, coupling 42 source parsers and 20 diverse low-resource testlanguages, show that ILPS outperforms KL and L2V on 13/20 and 14/20 testlanguages, respectively. Further, we show that by predicting the best parser"at the treebank level" (SBPS), using the aggregation of predictions from ourinstance-level model, we outperform the same baselines on 17/20 and 16/20 testlanguages.

Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers