In this paper, we describe a morpho-syntactic tagger of tweets, an important component of the CEA List DeepLIMA tool which is a multilingual text analysis platform based on deep learning. This tagger is built for the Morpho-syntactic Tagging of Tweets (MTT) Shared task of the 2018 VarDial Evaluation Campaign. The MTT task focuses on morpho-syntactic annotation of non-canonical Twitter varieties of three South-Slavic languages: Slovene, Croatian and Serbian. We propose to use a neural network model trained in an end-to-end manner for the three languages without any need for task or domain specific features engineering. The proposed approach combines both character and word level representations. Considering the lack of annotated data in the social media domain for South-Slavic languages, we have also implemented a cross-domain Transfer Learning (TL) approach to exploit any available related out-of-domain annotated data.

A reinterpretation of Head-Driven Phrase Structure Grammar (HPSG) in a proof-theoretic context is presented. This approach yields a decision procedure which can be used to establish whether certain strings are generated by a given HPSG grammar. It is possible to view HPSG as a fragment of linear logic (Girard, 1987), subject to partiality and side conditions on inference rules. This relates HPSG to several categorial logics (Morrill, 1990) . Specifically, HPSG signs are mapped onto quantified formulae, which can be interpreted as second-order types given the Curry-Howard isomorphism. The logic behind type inference will, aside from the usual quantifier introduction and elimination rules, consist of a partial logic for the undirected implication connective. It will be shown how this logical perspective can be turned into a parsing perspective. The enterprise takes the standard HPSG of Pollard – Sag (1987) as a starting point, since this version of HPSG is well-documented and has been around long enough to have displayed both merits and shortcomings; the approach is directly applicable to more recent versions of HPSG, however. In order to make the proof-theoretic recasting smooth, standard HPSG is reformulated in a binary format.