  This paper describes the ICS PAS system which took part in CoNLL 2018 shared
task on Multilingual Parsing from Raw Text to Universal Dependencies. The
system consists of jointly trained tagger, lemmatizer, and dependency parser
which are based on features extracted by a biLSTM network. The system uses both
fully connected and dilated convolutional neural architectures. The novelty of
our approach is the use of an additional loss function, which reduces the
number of cycles in the predicted dependency graphs, and the use of
self-training to increase the system performance. The proposed system, i.e. ICS
PAS (Warszawa), ranked 3th/4th in the official evaluation obtaining the
following overall results: 73.02 (LAS), 60.25 (MLAS) and 64.44 (BLEX).
