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Evaluate Similarity of Requirements with Multilingual Natural Language Processing

Published online by Cambridge University Press:  26 May 2022

U. Bisang ,
J. Brünnhäußer ,
P. Lünnemann ,
L. Kirsch  and
K. Lindow
U. Bisang
Affiliation:
Fraunhofer IPK, Germany
J. Brünnhäußer*
Affiliation:
Fraunhofer IPK, Germany
P. Lünnemann
Affiliation:
Fraunhofer IPK, Germany
L. Kirsch
Affiliation:
CONTACT Software GmbH, Germany
K. Lindow
Affiliation:
Fraunhofer IPK, Germany
*
joerg.bruennhaeusser@ipk.fraunhofer.de

Abstract

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Finding redundant requirements or semantically similar ones in previous projects is a very time-consuming task in engineering design, especially with multilingual data. Due to modern NLP it is possible to automate such tasks. In this paper we compared different multilingual embeddings models to see which of them is the most suitable to find similar requirements in English and German. The comparison was done for both in-domain data (requirements pairs) and out-of-domain data (general sentence pairs). The most suitable model were sentence embeddings learnt with knowledge distillation.

Keywords

artificial intelligence (AI)requirements managementinformation managementdata-driven designnatural language processing
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1511 - 1520
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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