2019
EMNLP
EMNLP 2019
Answering Conversational Questions on Structured Data without Logical Forms
Abstract
AbstractWe 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.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🐣
Hot Topic Early Bird
— table question answering
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Deep Learning > Architectures > Graph Neural Networks
Natural Language Processing > Generation > Dialogue Systems
Natural Language Processing > Applications > Question Answering
Machine Learning > Learning Types > Few-Shot Learning
Natural Language Processing > Applications > Dialogue Systems
Artificial Intelligence > Core AI > Knowledge Graph