  We present a novel approach to answering sequential questions based on
structured objects such as knowledge bases or tables without using a logical
form as an intermediate representation. We encode tables as graphs using a
graph neural network model based on the Transformer architecture. The answers
are then selected from the encoded graph using a pointer network. This model is
appropriate for processing conversations around structured data, where the
attention mechanism that selects the answers to a question can also be used to
resolve conversational references. We demonstrate the validity of this approach
with competitive results on the Sequential Question Answering (SQA) task (Iyyer
et al., 2017).
