  Recent studies have shown that neural models can achieve high performance on
several sequence labelling/tagging problems without the explicit use of
linguistic features such as part-of-speech (POS) tags. These models are trained
only using the character-level and the word embedding vectors as inputs. Others
have shown that linguistic features can improve the performance of neural
models on tasks such as chunking and named entity recognition (NER). However,
the change in performance depends on the degree of semantic relatedness between
the linguistic features and the target task; in some instances, linguistic
features can have a negative impact on performance. This paper presents an
approach to jointly learn these linguistic features along with the target
sequence labelling tasks with a new multi-task learning (MTL) framework called
Gated Tasks Interaction (GTI) network for solving multiple sequence tagging
tasks. The GTI network exploits the relations between the multiple tasks via
neural gate modules. These gate modules control the flow of information between
the different tasks. Experiments on benchmark datasets for chunking and NER
show that our framework outperforms other competitive baselines trained with
and without external training resources.
