Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Anish Mahendran"'
Publikováno v:
AIP Conference Proceedings.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e5724728f069a9e19b53764676d940e
https://doi.org/10.1063/5.0117630
https://doi.org/10.1063/5.0117630
Publikováno v:
COLING
Slot-filling models in task-driven dialog systems rely on carefully annotated training data. However, annotations by crowd workers are often inconsistent or contain errors. Simple solutions like manually checking annotations or having multiple worker
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4962731f41bccce66acdae832796958c
https://doi.org/10.18653/v1/2020.coling-main.442
https://doi.org/10.18653/v1/2020.coling-main.442
Autor:
Anthony Zheng, Stefan Larson, Rishi Tekriwal, Eric Guldan, Anish Mahendran, Jonathan K. Kummerfeld, Adrian Cheung, Kevin Leach
Publikováno v:
EMNLP (1)
Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts. We propose a general approach for guiding workers to write more diverse text by iteratively const
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e0bfb19c210ce4d229e92f01a1e95b2
https://doi.org/10.18653/v1/2020.emnlp-main.650
https://doi.org/10.18653/v1/2020.emnlp-main.650
Autor:
Jason Mars, Johann Hauswald, Parker Hill, Michael A. Laurenzano, Stefan Larson, Lingjia Tang, Jonathan K. Kummerfeld, Andrew Lee, Anish Mahendran
Publikováno v:
NAACL-HLT (1)
In a corpus of data, outliers are either errors: mistakes in the data that are counterproductive, or are unique: informative samples that improve model robustness. Identifying outliers can lead to better datasets by (1) removing noise in datasets and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40c3f71aae02474cb2ef9b5ccd24156b
http://arxiv.org/abs/1904.03122
http://arxiv.org/abs/1904.03122
Autor:
Jason Mars, Jonathan K. Kummerfeld, Joseph Peper, Anish Mahendran, Andrew Lee, Lingjia Tang, Kevin Leach, Michael A. Laurenzano, Stefan Larson, Christopher Clarke, Parker Hill
Publikováno v:
EMNLP/IJCNLP (1)
Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26f395a7eddd6eb0fc8df71e1cd98b3c