Latest revision as of 05:52, 2 February 2021

Abstract

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1145/3178876.3186023

• http://publica.fraunhofer.de/documents/N-499989.html

• http://dl.acm.org/ft_gateway.cfm?id=3186023&ftid=1957391&dwn=1,

http://dx.doi.org/10.1145/3178876.3186023 under the license http://www.acm.org/publications/policies/copyright_policy#Background

• https://dblp.uni-trier.de/db/conf/www/www2018.html#SinghRBSLUVKP0V18,

https://dl.acm.org/citation.cfm?doid=3178876.3186023,

https://dl.acm.org/citation.cfm?id=3186023,

https://doi.org/10.1145/3178876.3186023,

https://academic.microsoft.com/#/detail/2788460763