dc.contributor.author | Pasricha, Nivranshu | |
dc.contributor.author | Arcan, Mihael | |
dc.contributor.author | Buitelaar, Paul | |
dc.date.accessioned | 2021-07-29T10:07:19Z | |
dc.date.available | 2021-07-29T10:07:19Z | |
dc.date.issued | 2021-08-05 | |
dc.identifier.citation | Pasricha, Nivranshu, Arcan, Mihael, & Buitelaar, Paul. (2021). NUIG-DSI’s submission to the GEM Benchmark 2021. Paper presented at the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Online, 05-06 August. doi:10.18653/v1/2021.gem-1.13 | en_IE |
dc.identifier.uri | http://hdl.handle.net/10379/16886 | |
dc.description.abstract | This paper describes the submission by NUIG-DSI to the GEM benchmark 2021. We participate in the modeling shared task where we submit outputs on four datasets for data-to-text generation, namely, DART, WebNLG (en), E2E and CommonGen. We follow an approach similar to the one described in the GEM benchmark paper where we use the pre-trained T5-base model for our submission. We train this model on additional monolingual data where we experiment with different masking strategies specifically focused on masking entities, predicates and concepts as well as a random masking strategy for pre-training. In our results we find that random masking performs the best in terms of automatic evaluation metrics, though the results are not statistically significantly different compared to other masking strategies. | en_IE |
dc.description.sponsorship | This work was conducted with the financial support of the Science Foundation Ireland Centre for
Research Training in Artificial Intelligence under Grant No. 18/CRT/6223 and co-supported by
Science Foundation Ireland under grant number
SFI/12/RC/2289 2 (Insight), co-funded by the European Regional Development Fund. | en_IE |
dc.format | application/pdf | en_IE |
dc.language.iso | en | en_IE |
dc.publisher | Association for Computational Linguistics | en_IE |
dc.relation.ispartof | Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) | en |
dc.rights | Attribution 4.0 International (CC BY 4.0) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | GEM benchmark | en_IE |
dc.title | NUIG-DSI’s submission to the GEM Benchmark 2021 | en_IE |
dc.type | Conference Paper | en_IE |
dc.date.updated | 2021-07-29T09:40:18Z | |
dc.identifier.doi | 10.18653/v1/2021.gem-1.13 | |
dc.local.publishedsource | https://dx.doi.org/10.18653/v1/2021.gem-1.13 | en_IE |
dc.description.peer-reviewed | peer-reviewed | |
dc.contributor.funder | Science Foundation Ireland | en_IE |
dc.contributor.funder | European Regional Development Fund | en_IE |
dc.internal.rssid | 26418933 | |
dc.local.contact | Nivranshu Pasricha, 103 Nlp Unit, , Data Science Institute, , Ida Business Park, , Lower Dangan, Galway. Email: n.pasricha1@nuigalway.ie | |
dc.local.copyrightchecked | Yes | |
dc.local.version | PUBLISHED | |
dcterms.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ | en_IE |
nui.item.downloads | 48 | |