The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The WebNLG Challenge: Generating Text from RDF Data
Open accessAbstract
The WebNLG challenge consists in mapping sets of RDF triples to text.It provides a common benchmark on which to train, evaluate and compare "microplanners", i.e. generation systems that verbalise a given content by making a range of complex interacting choices including referring expression generation, aggregation, lexicalisation, surface realisation and sentence segmentation.In this paper, we introduce the microplanning task, describe data preparation, introduce our evaluation methodology, analyse participant results and provide a brief description of the participating systems.(3) a. LOCATION-COUNTRY-STARTDATE ⇒ Passive-Apposition-Active
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“And the evaluation tasks include WebNLG (Gardent et al., 2017) for conditional generation, GLUE (Wang et al., 2018) and Super-GLUE (Wang et al., 2019) for natural language understanding; SemEval (Hendrickx et al., 2010) for relation extraction; Few-NERD (Ding et al., 2021b) for fine-grained entity typing; MNLI (William…”
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ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sources
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Metadata
- Authors
- Gardent, C., Shimorina, A., Narayan, S., & Perez-Beltrachini, L.
- Venue
- Proceedings of the 10th International Conference on Natural Language Generation
- Published
- 2017
- Pages
- 124-133
- OpenAlex
- W2786660442
- Cited by
- 342
- References
- 2
- FWCI
- 11.94
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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