# A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints

Source: [A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints](<https://www.sota2.com/research/paper/68fa66361e209131cea0ecd7>)

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

Data updated: 2025-10-28T13:56:39.992Z

Authors: Yosi Mass, Haggai Roitman, Shai Erera, Or Rivlin, Bar Weiner, David Konopnicki

Published: 2019-08-19T13:14:02.000Z

## Abstract

We study the use of BERT for non-factoid question-answering, focusing on the passage re-ranking task under varying passage lengths. To this end, we explore the fine-tuning of BERT in different learning-to-rank setups, comprising both point-wise and pair-wise methods, resulting in substantial improvements over the state-of-the-art. We then analyze the effectiveness of BERT for different passage lengths and suggest how to cope with large passages.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1908.06780>)

[PDF](<https://arxiv.org/pdf/1908.06780.pdf>)