  We take a practical approach to solving sequence labeling problem assuming
unavailability of domain expertise and scarcity of informational and
computational resources. To this end, we utilize a universal end-to-end
Bi-LSTM-based neural sequence labeling model applicable to a wide range of NLP
tasks and languages. The model combines morphological, semantic, and structural
cues extracted from data to arrive at informed predictions. The model's
performance is evaluated on eight benchmark datasets (covering three tasks:
POS-tagging, NER, and Chunking, and four languages: English, German, Dutch, and
Spanish). We observe state-of-the-art results on four of them: CoNLL-2012
(English NER), CoNLL-2002 (Dutch NER), GermEval 2014 (German NER), Tiger Corpus
(German POS-tagging), and competitive performance on the rest.
