webnlg
t5-qg_webnlg_synth-ent5-qa_webnlg_synth-enbi_all_bs160_allneg_finetuned_WebNLG2020_correctnesscross_all_bs160_allneg_finetuned_WebNLG2020_data_coveragebi_all_bs160_allneg_finetuned_WebNLG2020_relevancebi_all_bs320_vanilla_finetuned_WebNLG2020_relevancecross_all_bs192_hardneg_finetuned_WebNLG2020_data_coveragecross_all_bs320_vanilla_finetuned_WebNLG2020_metric_average
Datasets
All datasets matching “webnlg”web_nlgWebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets
and short texts that cover about 450 different DBpedia properties. The WebNLG data
was originally created to promote the development of RDF verbalisers able to
generate short text and to handle micro-planning (i.e., sentence segmentation and
ordering, referring expression generation, aggregation); the goal of the task is
to generate texts starting from 1 to 7 input triples which have entities in common
(so the input is actually a connected Knowledge Graph). The dataset contains about
17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets
and 19,000 crowdsourced texts in Russian. A challenging test set section with
entities and/or properties that have not been seen at training time is available.web_nlgThe WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).challenge-2023The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).webnlgwebnlg_starwebnlg-data2text