We propose UDP , the first training-free parser for Universal Dependencies ( UD ) . Our algorithm is based on PageRank and a small set of head attachment rules . It features two-step decoding to guarantee that function words are attached as leaf nodes . The parser requires no training , and it is competitive with a delexicalized transfer system . UDP offers a linguistically sound unsupervised alternative to cross-lingual parsing for UD , which can be used as a baseline for such systems . The parser has very few parameters and is distinctly robust to domain change across languages . Grammar induction and unsupervised dependency parsing are active fields of research in natural language processing ( Klein and Manning , 2004 ; Gelling et al . , 2012 ) . However , many data-driven approaches struggle with learning relations that match the conventions of the test data , e.g. , Klein and Manning reported the tendency of their DMV parser to make determiners the heads of German nouns , which would not be an error if the test data used a DP analysis ( Abney , 1987 ) . Even supervised transfer approaches ( McDonald et al . , 2011 ) suffer from target adaptation problems when facing word order differences . The Universal Dependencies ( UD ) project ( Nivre et al . , 2015 ; Nivre et al . , 2016 ) offers a dependency formalism that aims at providing a consistent representation across languages , while enforcing a few hard constraints . The arrival of such treebanks , expanded and improved on a regular basis , provides a new milestone for crosslingual dependency parsing research ( McDonald et al . , 2013 ) . Furthermore , given that UD rests on a series of simple principles like the primacy of lexical heads , cf . Johannsen et al . ( 2015 ) for more details , we expect that such a formalism lends itself more naturally to a simple and linguistically sound rulebased approach to cross-lingual parsing . In this paper we present such an approach . Our system is a dependency parser that requires no training , and relies solely on explicit part-ofspeech ( POS ) constraints that UD imposes . In particular , UD prescribes that trees are single-rooted , and that function words like adpositions , auxiliaries , and determiners are always dependents of content words , while other formalisms might treat them as heads ( De Marneffe et al . , 2014 ) . We ascribe our work to the viewpoints of Bender ( 2009 ) about the incorporation of linguistic knowledge in language-independent systems . We have presented UDP , an unsupervised dependency parser for Universal Dependencies ( UD ) that makes use of personalized PageRank and a small set of head-dependent rules . The parser requires no training data and estimates adposition direction directly from the input . We achieve competitive performance on all but two UD languages , and even beat a multi-source delexicalized parser ( MSD ) on Hindi . We evaluated the parser on three POS setups and across domains . Our results show that UDP is less affected by deteriorating POS tags than MSD , and is more resilient to domain changes . Given how much of the overall dependency structure can be explained by this fairly system , we propose UDP as an additional UD parsing baseline . The parser , the in-house annotated test sets , and the domain data splits are made freely available . 6UD is a running project , and the guidelines are bound to evolve overtime . Indeed , the UD 2.0 guidelines have been recently released . UDP can be augmented with edge labeling for some deterministic labels like case or det . Some further constrains can be incorporated in UDP . Moreover , the parser makes no special treatment of multiword expression that would require a lexicon , coordinations or proper names . All these three kinds of structures have a flat tree where all words depend on the leftmost one . While coordination attachment is a classical problem in parsing and out of the scope of our work , a proper name sequence can be straightforwardly identified from the partof-speech tags , and it falls thus in the area of structures predictable using simple heuristics . Moreover , our use of PageRank could be expanded to directly score the potential dependency edges instead of words , e.g. , by means of edge reification .

Challenge: For dependency parsing, unsupervised methods struggle with learning relations that match conventions of the test data and supervised counterparts suffer from word order target adaptation.
Approach: They propose an unsupervised approach based on PageRank and a set of head attachment rules that solely depend on explicit part-of-speech constraints from Universal Dependencies.
Outcome: The proposed linguistically sound method performs competitively with a delexicalized transfer system while having few parameters and robustness to domain changes across languages.