SwePub
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "WFRF:(Shimorina Anastasia) "

Sökning: WFRF:(Shimorina Anastasia)

  • Resultat 1-3 av 3
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
1.
  • Castro Ferreira, Thiago, et al. (författare)
  • The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)
  • 2020
  • Ingår i: Proceedings of the WebNLG+, 3rd Workshop on Natural Language Generation from the Semantic Web, Dublin 18 December 2020. - Stroudsburg : Association for Computational Linguistics. - 9781952148590
  • Konferensbidrag (refereegranskat)abstract
    • WebNLG+ offers two challenges: (i) mapping sets of RDF triples to English or Russian text (generation) and (ii) converting English or Russian text to sets of RDF triples (semantic parsing). Compared to the eponymous WebNLG challenge, WebNLG+ provides an extended dataset that enables the training, evaluation, and comparison of microplanners and semantic parsers. In this paper, we present the results of the generation and semantic parsing task for both English and Russian and provide a brief description of the participating systems.
  •  
2.
  • Gehrmann, Sebastian, et al. (författare)
  • The GEM Benchmark : Natural Language Generation, its Evaluation and Metrics
  • 2021
  • Ingår i: The 1st Workshop on Natural Language Generation, Evaluation, and Metrics. - Stroudsburg, PA, USA : Association for Computational Linguistics. ; , s. 96-120
  • Konferensbidrag (refereegranskat)abstract
    • We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.
  •  
3.
  • Moussallem, Diego, et al. (författare)
  • A General Benchmarking Framework for Text Generation
  • 2020
  • Ingår i: Proceedings of the WebNLG+, 3rd Workshop on Natural Language Generation from the Semantic Web, Dublin 18 December 2020. - : Association for Computational Linguistics.
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • The RDF-to-text task has recently gained substantial attention due to the continuous growth of RDF knowledge graphs in number and size. Recent studies have focused on systematically comparing RDF-to-text approaches on benchmarking datasets such as WebNLG. Although some evaluation tools have already been proposed for text generation, none of the existing solutions abides by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and involves RDF data for the knowledge extraction task. In this paper, we present BENG, a FAIR benchmarking platform for Natural Language Generation (NLG) and Knowledge Extraction systems with focus on RDF data. BENG builds upon the successful benchmarking platform GERBIL, is open-source and is publicly available along with the data it contains.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-3 av 3

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy