Conversational Question Answering over Passages by Leveraging Word Proximity Networks

• URL: http://arxiv.org/abs/2004.13117v3
• Date: Mon, 25 May 2020 15:21:00 GMT
• Title: Conversational Question Answering over Passages by Leveraging Word Proximity Networks
• Authors: Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
• Abstract summary: CROWN is an unsupervised yet effective system for conversational QA with passage responses. It supports several modes of context propagation over multiple turns. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods.
• Score: 33.59664244897881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Question answering (QA) over text passages is a problem of long-standing interest in information retrieval. Recently, the conversational setting has attracted attention, where a user asks a sequence of questions to satisfy her information needs around a topic. While this setup is a natural one and similar to humans conversing with each other, it introduces two key research challenges: understanding the context left implicit by the user in follow-up questions, and dealing with ad hoc question formulations. In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns. To this end, CROWN first builds a word proximity network (WPN) from large corpora to store statistically significant term co-occurrences. At answering time, passages are ranked by a combination of their similarity to the question, and coherence of query terms within: these factors are measured by reading off node and edge weights from the WPN. CROWN provides an interface that is both intuitive for end-users, and insightful for experts for reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods.

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