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1
Combining contextualized and non-contextualized embeddings for domain adaptation and beyond
Pörner, Nina Mareike [Verfasser]; Schütze, Hinrich [Akademischer Betreuer]. - München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021
DNB Subject Category Language
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2
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity ...
Poerner, Nina; Waltinger, Ulli; Schütze, Hinrich. - : Association for Computational Linguistics, 2020
BASE
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3
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Miyao, Yusuke; Poerner, Nina; Roth, Benjamin; Gurevych, Iryna; Schütze, Hinrich. - : Ludwig-Maximilians-Universität München, 2018
Abstract: The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable.We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the mosteffective explanation methods and recommend them for explaining DNNs in NLP.
Keyword: ddc:000; ddc:004; ddc:400; ddc:410
URL: http://nbn-resolving.de/urn:nbn:de:bvb:19-epub-61866-4
https://epub.ub.uni-muenchen.de/61866/
https://doi.org/10.5282/ubm/epub.61866
https://epub.ub.uni-muenchen.de/61866/1/1801.06422.pdf
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4
Analysis and classification of cooperative and competitive dialogs
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