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MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation

Proceedings of the AAAI Conference on Artificial Intelligence(2020)10.1609/aaai.v34i05.6339Source: DataRank Database

MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation is a research paper published in Proceedings of the AAAI Conference on Artificial Intelligence (2020). On theSindex it has a DataRank of 0. It has been cited 34 times.

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Open Access34 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (9 grants)OA: goldTopics (7)IDs (PubMed, PMC)

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Authors (3)

Xun Wang ORCID,Hong Yu ORCID,Rumeng Li ORCID

Live enrichment

PubMed:34094698 PMC:PMC8174838

NIDA NIH HHS

Grant: R01 DA045816

NHLBI NIH HHS

Grant: R01 HL137794

NHLBI NIH HHS

Grant: R01 HL135219

NHLBI NIH HHS

Grant: R01 HL125089

NLM NIH HHS

Grant: R01 LM012817

National Institutes of Health

Grant: 5R01HL137794-03

Detecting bleeding events using EHRs for prediction in Afib

National Institutes of Health

Grant: 1R01LM012817-01A1

Resource Curation and Evaluation for EHR Note Comprehension

National Institutes of Health

Grant: 1R01HL135219-01

Community Surveillance of Coronary Heart Disease

National Institutes of Health

Grant: 5R01DA045816-04

Identifying Opioid Overdose Predictors using EHRs

goldLicense: https://www.aaai.org

publisherhttps://ojs.aaai.org/index.php/AAAI/article/download/6339/6195

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8174838

https://doi.org/10.1609/aaai.v34i05.6339

https://dx.doi.org/10.48550/arxiv.1912.05467

https://pubmed.ncbi.nlm.nih.gov/34094698

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

FOS: Computer and information sciencesComputer Science - Machine LearningComputer Science - Computation and LanguageComputation and Language (cs.CL)Machine Learning (cs.LG)