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pro vyhledávání: '"Forster, Martina"'
Autor:
Forster, Martina, Schulz, Claudia, Nokku, Prudhvi, Mirsafian, Melicaalsadat, Kasundra, Jaykumar, Skylaki, Stavroula
Multi-Label Classification (MLC) is a common task in the legal domain, where more than one label may be assigned to a legal document. A wide range of methods can be applied, ranging from traditional ML approaches to the latest Transformer-based archi
Externí odkaz:
http://arxiv.org/abs/2401.11852
For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types
Externí odkaz:
http://arxiv.org/abs/2304.02787
Publikováno v:
Proceedings of ACL-IJCNLP 2021
Beam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does not reflect interactions between candidates. Empirically
Externí odkaz:
http://arxiv.org/abs/2106.07400
Neural sequence-to-sequence models are currently the predominant choice for language generation tasks. Yet, on word-level tasks, exact inference of these models reveals the empty string is often the global optimum. Prior works have speculated this ph
Externí odkaz:
http://arxiv.org/abs/2102.08424
Publikováno v:
International Perspectives on Teaching & Learning with GIS in Secondary Schools; 2012, p207-214, 8p