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Exploring neural question generation for formal pragmatics: Data set and model evaluation

We provide the first openly-available German QUestion-Answer Congruence Corpus (QUACC), designed for the task of sentence-based question generation with question-answer congruence. Based on this corpus, we establish suitable baselines for question generation, comparing systems of very different natu...

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Autores principales: De Kuthy, Kordula, Kannan, Madeeswaran, Santhi Ponnusamy, Haemanth, Meurers, Detmar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9661521/
https://www.ncbi.nlm.nih.gov/pubmed/36388400
http://dx.doi.org/10.3389/frai.2022.966013
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author De Kuthy, Kordula
Kannan, Madeeswaran
Santhi Ponnusamy, Haemanth
Meurers, Detmar
author_facet De Kuthy, Kordula
Kannan, Madeeswaran
Santhi Ponnusamy, Haemanth
Meurers, Detmar
author_sort De Kuthy, Kordula
collection PubMed
description We provide the first openly-available German QUestion-Answer Congruence Corpus (QUACC), designed for the task of sentence-based question generation with question-answer congruence. Based on this corpus, we establish suitable baselines for question generation, comparing systems of very different nature. Question generation is an interesting challenge in particular for current neural network architectures given that it combines aspects of language meaning and forms in complex ways. The systems have to generate question phrases appropriately linking to the meaning of the envisaged answer phrases, and they have to learn to generate well-formed questions using the source. We show that our QUACC corpus is well-suited to investigate the performance of various neural models and gain insights about the specific error sources.
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spelling pubmed-96615212022-11-15 Exploring neural question generation for formal pragmatics: Data set and model evaluation De Kuthy, Kordula Kannan, Madeeswaran Santhi Ponnusamy, Haemanth Meurers, Detmar Front Artif Intell Artificial Intelligence We provide the first openly-available German QUestion-Answer Congruence Corpus (QUACC), designed for the task of sentence-based question generation with question-answer congruence. Based on this corpus, we establish suitable baselines for question generation, comparing systems of very different nature. Question generation is an interesting challenge in particular for current neural network architectures given that it combines aspects of language meaning and forms in complex ways. The systems have to generate question phrases appropriately linking to the meaning of the envisaged answer phrases, and they have to learn to generate well-formed questions using the source. We show that our QUACC corpus is well-suited to investigate the performance of various neural models and gain insights about the specific error sources. Frontiers Media S.A. 2022-10-31 /pmc/articles/PMC9661521/ /pubmed/36388400 http://dx.doi.org/10.3389/frai.2022.966013 Text en Copyright © 2022 De Kuthy, Kannan, Santhi Ponnusamy and Meurers. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
De Kuthy, Kordula
Kannan, Madeeswaran
Santhi Ponnusamy, Haemanth
Meurers, Detmar
Exploring neural question generation for formal pragmatics: Data set and model evaluation
title Exploring neural question generation for formal pragmatics: Data set and model evaluation
title_full Exploring neural question generation for formal pragmatics: Data set and model evaluation
title_fullStr Exploring neural question generation for formal pragmatics: Data set and model evaluation
title_full_unstemmed Exploring neural question generation for formal pragmatics: Data set and model evaluation
title_short Exploring neural question generation for formal pragmatics: Data set and model evaluation
title_sort exploring neural question generation for formal pragmatics: data set and model evaluation
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9661521/
https://www.ncbi.nlm.nih.gov/pubmed/36388400
http://dx.doi.org/10.3389/frai.2022.966013
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