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Author: Avi Bleiweiss

Affiliation: BShalem Research, Sunnyvale, U.S.A.

Keyword(s): Question Answering, Evidence Natural Split, Transformers, Language Model, Deep Learning.

Abstract: Recently published datasets for open-domain question answering follow question elicitation from a fairly small snippet of Wikipedia content. Often centered around an article section, the evidence is further subdivided into context-unaware passages of uniform token-lengths to found the basic retrieval units. In this study we hypothesized that splitting a section perceived as an opaque text fragment may hinder quality of answer span predictions. We propose to dynamically draw content corresponding to an article-section url from the most updated online Wikipedia rather than from an archived snapshot. Hence approaching space complexity of O(1), downward from O(n) for a dataset that is fully populated with static context. We then parse the url bound content and feed our neural retriever with a list of paragraph-like html elements that preserve context boundaries naturally. Using knowledge distillation from a sustainable language model pretrained on the large SQuAD 2.0 dataset to the state -of-the-art QuAC domain, shows that during inference our natural context split recovered answer span predictions by 7.5 F1 and 4.1 EM points over a synthetic distribution of fixed-length passages. (More)

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Paper citation in several formats:
Bleiweiss, A. (2022). Dynamically Generated Question Answering Evidence using Efficient Context-preserving Subdivision. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 276-284. DOI: 10.5220/0010815700003116

@conference{icaart22,
author={Avi Bleiweiss.},
title={Dynamically Generated Question Answering Evidence using Efficient Context-preserving Subdivision},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={276-284},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010815700003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Dynamically Generated Question Answering Evidence using Efficient Context-preserving Subdivision
SN - 978-989-758-547-0
IS - 2184-433X
AU - Bleiweiss, A.
PY - 2022
SP - 276
EP - 284
DO - 10.5220/0010815700003116
PB - SciTePress

- Science and Technology Publications, Lda.
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