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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2022.966013/full |
| _version_ | 1817977716350124032 |
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| author | Kordula De Kuthy Madeeswaran Kannan Haemanth Santhi Ponnusamy Detmar Meurers |
| author_facet | Kordula De Kuthy Madeeswaran Kannan Haemanth Santhi Ponnusamy Detmar Meurers |
| author_sort | Kordula De Kuthy |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T22:20:02Z |
| format | Article |
| id | doaj.art-60af75f3934d4bc48d336ad6b4a27d48 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-13T22:20:02Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-60af75f3934d4bc48d336ad6b4a27d482022-12-22T02:27:16ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122022-10-01510.3389/frai.2022.966013966013Exploring neural question generation for formal pragmatics: Data set and model evaluationKordula De KuthyMadeeswaran KannanHaemanth Santhi PonnusamyDetmar MeurersWe 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.https://www.frontiersin.org/articles/10.3389/frai.2022.966013/fullquestion generationGermanquestion-answer datasetQuestions under Discussiondiscourse analysisneural network |
| spellingShingle | Kordula De Kuthy Madeeswaran Kannan Haemanth Santhi Ponnusamy Detmar Meurers Exploring neural question generation for formal pragmatics: Data set and model evaluation Frontiers in Artificial Intelligence question generation German question-answer dataset Questions under Discussion discourse analysis neural network |
| 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 | question generation German question-answer dataset Questions under Discussion discourse analysis neural network |
| url | https://www.frontiersin.org/articles/10.3389/frai.2022.966013/full |
| work_keys_str_mv | AT korduladekuthy exploringneuralquestiongenerationforformalpragmaticsdatasetandmodelevaluation AT madeeswarankannan exploringneuralquestiongenerationforformalpragmaticsdatasetandmodelevaluation AT haemanthsanthiponnusamy exploringneuralquestiongenerationforformalpragmaticsdatasetandmodelevaluation AT detmarmeurers exploringneuralquestiongenerationforformalpragmaticsdatasetandmodelevaluation |