Modeling transitions of focal entities for conversational knowledge base question answering

Modeling transitions of focal entities for conversational knowledge base question answering

Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal ent...

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Bibliographic Details
Main Authors: LAN, Yunshi, JIANG, Jing
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Computational linguistics
Graphic methods
Knowledge based systems
Natural language processing systems
Probability distributions
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/6779
https://ink.library.smu.edu.sg/context/sis_research/article/7782/viewcontent/2021.acl_long.255.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method.

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