Difference between revisions of "Data sets for NLG blog"
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=== Weathergov === | === Weathergov === | ||
The Weathergov corpus contains the output of a template-based weather forecast generator, not human-written forecasts ([https://ehudreiter.com/2017/05/09/weathergov/ blog post]). Hence ML on Weathergov is an exercise in reverse engineering a template-based NLG system, not in training an NLG system from human data. If you want to train on human-written weather forecasts, consider using the [https://ehudreiter.files.wordpress.com/2016/12/sumtime.zip SumTime corpus] instead. | The Weathergov corpus contains the output of a template-based weather forecast generator, not human-written forecasts ([https://ehudreiter.com/2017/05/09/weathergov/ blog post]). Hence ML on Weathergov is an exercise in reverse engineering a template-based NLG system, not in training an NLG system from human data. If you want to train on human-written weather forecasts, consider using the [https://ehudreiter.files.wordpress.com/2016/12/sumtime.zip SumTime corpus] instead. | ||
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| + | === Tuna === | ||
| + | [http://www.lrec-conf.org/proceedings/lrec2010/pdf/251_Paper.pdf Dutch] and [https://www.aclweb.org/anthology/W17-3532 Mandarin] versions of Tuna have been developed. | ||
Revision as of 07:46, 21 August 2019
This blog is a supplement to Data sets for NLG, which lists comments about these data sets from users, authors and other interested parties. We are especially interested in comments about appropriate and inappropriate usage of a data set, "best practice" use of a data set, useful additional information about a data set (eg, scope, how it was constructed), and pointers to related data sets which may be more appropriate for some users. Links to relevant papers and other resources are welcome
E2E
The E2E dataset was used in the E2E challenge.
SumTime
A good example of the use of Sumtime is Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models.
Weathergov
The Weathergov corpus contains the output of a template-based weather forecast generator, not human-written forecasts (blog post). Hence ML on Weathergov is an exercise in reverse engineering a template-based NLG system, not in training an NLG system from human data. If you want to train on human-written weather forecasts, consider using the SumTime corpus instead.