Parser Training with Heterogeneous Treebanks
Sara Stymne, Miryam de Lhoneux, Aaron Smith, Joakim Nivre · 2018
How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question.We start by investigating previously suggested, but little evaluated, strategies for exploiting multiple treebanks based on concatenating training sets, with or without fine-tuning.We go on to propose a new method based on treebank embeddings.We perform experiments for several languages and show that in many cases fine-tuning and treebank embeddings lead to substantial improvements over single treebanks or concatenation, with average gains of 2.0-3.5 LAS points.We argue that treebank embeddings should be preferred due to their conceptual simplicity, flexibility and extensibility.