Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks

In conversational question answering, users express their information needs through a series of utterances with incomplete context. Typical ConvQA methods rely on a single source (a knowledge base (KB), or a text corpus, or a set of tables), thus being unable to benefit from increased answer coverag...

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Bibliographic Details
Published in:arXiv.org 2023-07
Main Authors: Christmann, Philipp, Rishiraj Saha Roy, Weikum, Gerhard
Format: Article
Language:English
Subjects:
End users
Graph neural networks
Knowledge bases (artificial intelligence)
Questions
Redundancy
Online Access:Get full text
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Summary:In conversational question answering, users express their information needs through a series of utterances with incomplete context. Typical ConvQA methods rely on a single source (a knowledge base (KB), or a text corpus, or a set of tables), thus being unable to benefit from increased answer coverage and redundancy of multiple sources. Our method EXPLAIGNN overcomes these limitations by integrating information from a mixture of sources with user-comprehensible explanations for answers. It constructs a heterogeneous graph from entities and evidence snippets retrieved from a KB, a text corpus, web tables, and infoboxes. This large graph is then iteratively reduced via graph neural networks that incorporate question-level attention, until the best answers and their explanations are distilled. Experiments show that EXPLAIGNN improves performance over state-of-the-art baselines. A user study demonstrates that derived answers are understandable by end users.
ISSN:2331-8422