Old School vs. New School : Comparing Transition-Based Parsers with and without Neural Network Enhancement

• In this paper, we attempt a comparison between "new school" transition-based parsers that use neural networks and their classical "old school" coun-terpart. We carry out experiments on treebanks from the Universal Depen-dencies project. To facilitate the comparison and analysis of results, we onlywork on a subset of those treebanks. However, we carefully select this sub-set in the hope to have results that are representative for the whole set oftreebanks. We select two parsers that are hopefully representative of the twoschools; MaltParser and UDPipe and we look at the impact of training sizeon the two models. We hypothesize that neural network enhanced modelshave a steeper learning curve with increased training size. We observe, how-ever, that, contrary to expectations, neural network enhanced models needonly a small amount of training data to outperform the classical models butthe learning curves of both models increase at a similar pace after that. Wecarry out an error analysis on the development sets parsed by the two sys-tems and observe that overall MaltParser suffers more than UDPipe fromlonger dependencies. We observe that MaltParser is only marginally betterthan UDPipe on a restricted set of short dependencies.

Ämnesord

HUMANIORA  -- Språk och litteratur -- Jämförande språkvetenskap och allmän lingvistik (hsv//swe)

HUMANITIES  -- Languages and Literature -- General Language Studies and Linguistics (hsv//eng)

Nyckelord

Computational Linguistics

Datorlingvistik

Publikations- och innehållstyp

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