TUdatalib : Training Data for Aspect-Controlled Neural Argument Generation

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dc.contributor.author	Schiller, Benjamin
dc.contributor.author	Gurevych, Iryna
dc.contributor.author	Daxenberger, Johannes
dc.date.accessioned	2020-05-07T16:47:03Z
dc.date.available	2020-05-07T16:47:03Z
dc.date.issued	2020-05
dc.identifier.uri	https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2329
dc.description	This collection holds (1) the argument aspect detection corpus, (2) the data to reproduce the training of the language models described in the paper, and (3) the arguments generated with both language models. The argument aspect detection corpus includes 5,032 arguments over eight controversial topics, annotated with aspects by crowdworkers. DISCLAIMER: All material provided in the archives reddit_training_data.7z and cc_training_data.7z may be used for research purposes only. For all other material in this collection (namely argument_aspect_detection_v1.0.7z and generated_arguments.7z), this restriction does not apply. The user acknowledges and agrees that the data is provided on an “as-is” basis and that the licensor makes no representations or warranties of any kind.	en_US
dc.language.iso	en	en_US
dc.relation	IsCitedBy;arXiv;2005.00084
dc.rights	In Copyright
dc.rights.uri	https://rightsstatements.org/vocab/InC/1.0/
dc.subject	argument generation	en_US
dc.subject	aspect detection	en_US
dc.subject	controlled argument generation	en_US
dc.title	Training Data for Aspect-Controlled Neural Argument Generation	en_US
dc.type	Dataset	en_US
dc.description.version	1.0	en_US
tud.project	DFG \| GU798/25-1 \| Offenes Argument-Min	en_US
tud.project	PTJ \| 03VP02540 \| ArgumenText	en_US