# WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval

Source: [WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval](<https://www.sota2.com/research/paper/68fa64641e209131cea04526>)

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

Data updated: 2025-10-27T05:54:50.324Z

Authors: Daniel Cohen, Liu Yang, W. Bruce Croft

Published: 2018-05-10T03:14:42.000Z

## Abstract

With the rise in mobile and voice search, answer passage retrieval acts as a critical component of an effective information retrieval system for open domain question answering. Currently, there are no comparable collections that address non-factoid question answering within larger documents while simultaneously providing enough examples sufficient to train a deep neural network. In this paper, we introduce a new Wikipedia based collection specific for non-factoid answer passage retrieval containing thousands of questions with annotated answers and show benchmark results on a variety of state of the art neural architectures and retrieval models. The experimental results demonstrate the unique challenges presented by answer passage retrieval within topically relevant documents for future research.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1805.03797>)

[PDF](<https://arxiv.org/pdf/1805.03797.pdf>)