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arXiv · 1908.11787

Answering Conversational Questions on Structured Data without Logical Forms

Thomas Müller·Francesco Piccinno·Massimo Nicosia·Peter Shaw·Yasemin Altun
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Abstract

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).

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Thomas Müller, Francesco Piccinno, Massimo Nicosia, Peter Shaw, Yasemin Altun. 2019-08-30. Answering Conversational Questions on Structured Data without Logical Forms. https://arxiv.org/abs/1908.11787

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