Answering Conversational Questions on Structured Data without Logical Forms

Understand

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.