Answering Conversational Questions on Structured Data without Logical Forms
Thomas M\"uller, Francesco Piccinno, Massimo Nicosia, Peter Shaw,, Yasemin Altun

TL;DR
This paper introduces a new method for answering conversational questions on structured data like knowledge bases and tables without relying on logical forms, using graph neural networks and pointer mechanisms.
Contribution
It proposes a novel approach that encodes structured data as graphs with Transformer-based GNNs and answers questions via pointer networks, bypassing logical form generation.
Findings
Achieved competitive results on the SQA benchmark
Effectively handles conversational references in structured data
Demonstrates the viability of graph-based encoding for QA
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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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
MethodsGraph Neural Network · Linear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Residual Connection · Byte Pair Encoding · Dense Connections · Label Smoothing · *Communicated@Fast*How Do I Communicate to Expedia? · Adam
