  This paper aims at improving how machines can answer questions directly from
text, with the focus of having models that can answer correctly multiple types
of questions and from various types of texts, documents or even from large
collections of them. To that end, we introduce the Weaver model that uses a new
way to relate a question to a textual context by weaving layers of recurrent
networks, with the goal of making as few assumptions as possible as to how the
information from both question and context should be combined to form the
answer. We show empirically on six datasets that Weaver performs well in
multiple conditions. For instance, it produces solid results on the very
popular SQuAD dataset (Rajpurkar et al., 2016), solves almost all bAbI tasks
(Weston et al., 2015) and greatly outperforms state-of-the-art methods for open
domain question answering from text (Chen et al., 2017).
