Target Language Adaptation of Discriminative Transfer Parsers
Oscar TÃ¤ckstrÃ¶m, Ryan McDonald and Joakim Nivre
We study multi-source transfer parsing for resource-poor target languages;
specifically methods for target language adaptation of delexicalized
discriminative graph-based dependency parsers. We first show how recent
insights on selective parameter sharing, based on typological and
language-family features, can be applied to a discriminative parser by
carefully decomposing its model features. We then show how the parser can be
relexicalized and adapted using unlabeled target language data and a learning
method that can incorporate diverse knowledge sources through ambiguous
labelings. In the latter scenario, we exploit two sources of knowledge: arc
marginals derived from the base parser in a self-training algorithm, and arc
predictions from multiple transfer parsers in an ensemble-training algorithm.
Our final model outperforms the state of the art in multi-source transfer
parsing on 15 out of 16 evaluated languages.
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