Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Stefan Larson"'
Autor:
Stefan Larson, Jacob Solawetz
Publikováno v:
EACL
Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::841ad8933f1ab9a8eab3a4d4c434decb
http://arxiv.org/abs/2101.11177
http://arxiv.org/abs/2101.11177
Publikováno v:
Document Analysis and Recognition – ICDAR 2021 Workshops ISBN: 9783030861582
ICDAR Workshops (2)
ICDAR Workshops (2)
To be robust enough for widespread adoption, document analysis systems involving machine learning models must be able to respond correctly to inputs that fall outside of the data distribution that was used to generate the data on which the models wer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a17b659a88386166934b0e1053db45cb
https://doi.org/10.1007/978-3-030-86159-9_30
https://doi.org/10.1007/978-3-030-86159-9_30
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