Parser Training with Heterogeneous Treebanks

Sara Stymne; Miryam de Lhoneux; Aaron Smith; Joakim Nivre

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arXiv · 1805.05089

Parser Training with Heterogeneous Treebanks

Sara Stymne·Miryam de Lhoneux·Aaron Smith·Joakim Nivre

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Abstract

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.

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Sara Stymne, Miryam de Lhoneux, Aaron Smith, Joakim Nivre. 2018-05-14. Parser Training with Heterogeneous Treebanks. https://arxiv.org/abs/1805.05089

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