  In conversational question answering, users express their information needs
through a series of utterances with incomplete context. Typical ConvQA methods
rely on a single source (a knowledge base (KB), or a text corpus, or a set of
tables), thus being unable to benefit from increased answer coverage and
redundancy of multiple sources. Our method EXPLAIGNN overcomes these
limitations by integrating information from a mixture of sources with
user-comprehensible explanations for answers. It constructs a heterogeneous
graph from entities and evidence snippets retrieved from a KB, a text corpus,
web tables, and infoboxes. This large graph is then iteratively reduced via
graph neural networks that incorporate question-level attention, until the best
answers and their explanations are distilled. Experiments show that EXPLAIGNN
improves performance over state-of-the-art baselines. A user study demonstrates
that derived answers are understandable by end users.
