Extending Neural Question Answering with Linguistic Input Features

Considerable progress in neural question answering has been made on competitive general domain datasets. In order to explore methods to aid the generalization potential of question answering models, we reimplement a state-of-the-art architecture, perform a parameter search on an open-domain dataset and evaluate a first approach for integrating linguistic input features such as part-of-speech tags, syntactic dependency relations and semantic roles. The results show that adding these input features has a greater impact on performance than any of the architectural parameters we explore. Our findings suggest that these layers of linguistic knowledge have the potential to substantially increase the generalization capacities of neural QA models, thus facilitating
cross-domain model transfer or the development of domain-agnostic QA models.