# Conversational Question Answering over Passages by Leveraging Word Proximity Networks

Source: [Conversational Question Answering over Passages by Leveraging Word Proximity Networks](<https://www.sota2.com/research/paper/68fa67991e209131cea1757c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:18:12.808Z

Authors: Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum

Published: 2020-04-27T19:30:47.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.13117>)

[PDF](<https://arxiv.org/pdf/2004.13117.pdf>)