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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 21 – 37 of 37 21 Uncovering Probabilistic Implications in Typological Knowledge Bases ... Bjerva, Johannes; Kementchedjhieva, Yova; Cotterell, Ryan. - : arXiv, 2019 BASE Show details 22 Back to the Future -- Sequential Alignment of Text Representations ... Bjerva, Johannes; Kouw, Wouter; Augenstein, Isabelle. - : arXiv, 2019 BASE Show details 23 Combining Sentiment Lexica with a Multi-View Variational Autoencoder ... Hoyle, Alexander; Wolf-Sonkin, Lawrence; Wallach, Hanna. - : arXiv, 2019 BASE Show details 24 Joint Emotion Label Space Modelling for Affect Lexica ... De Bruyne, Luna; Atanasova, Pepa; Augenstein, Isabelle. - : arXiv, 2019 BASE Show details 25 Unsupervised Discovery of Gendered Language through Latent-Variable Modeling ... Hoyle, Alexander; Wolf-Sonkin; Wallach, Hanna. - : arXiv, 2019 BASE Show details 26 What do Language Representations Really Represent? ... Bjerva, Johannes; Östling, Robert; Veiga, Maria Han. - : arXiv, 2019 BASE Show details 27 What Do Language Representations Really Represent? Augenstein, Isabelle; Östling, Robert; Veiga, Maria Han; Tiedemann, Jörg; Bjerva, Johannes In: Bjerva, Johannes; Östling, Robert; Veiga, Maria Han; Tiedemann, Jörg; Augenstein, Isabelle (2019). What Do Language Representations Really Represent? Computational Linguistics, 45(2):381-389. (2019) Abstract: A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships—a convenient benchmark used for evaluation in previous work—appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another. Keyword: 530 Physics; Artificial Intelligence; Computer Science Applications; Institute for Computational Science; Language and Linguistics; Linguistics and Language URL: https://www.zora.uzh.ch/id/eprint/185185/ https://doi.org/10.1162/coli_a_00351 https://doi.org/10.5167/uzh-185185 https://www.zora.uzh.ch/id/eprint/185185/1/coli_a_00351.pdf BASE Hide details 28 On evaluating embedding models for knowledge base completion Gemulla, Rainer; Wang, Yanjie; Broscheit, Samuel. - : Association for Computational Linguistics, 2019 BASE Show details 29 Specializing distributional vectors of all words for lexical entailment Ponti, Edoardo Maria; Kamath, Aishwarya; Pfeiffer, Jonas. - : Association for Computational Linguistics, 2019 BASE Show details 30 Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding ... Kementchedjhieva, Yova; Bjerva, Johannes; Augenstein, Isabelle. - : arXiv, 2018 BASE Show details 31 Parameter sharing between dependency parsers for related languages ... de Lhoneux, Miryam; Bjerva, Johannes; Augenstein, Isabelle. - : arXiv, 2018 BASE Show details 32 Jack the Reader - A Machine Reading Framework ... Weissenborn, Dirk; Minervini, Pasquale; Dettmers, Tim. - : arXiv, 2018 BASE Show details 33 From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings ... Bjerva, Johannes; Augenstein, Isabelle. - : arXiv, 2018 BASE Show details 34 Learning distributional token representations from visual features Gemulla, Rainer; Broscheit, Samuel; Keuper, Margret. - : Association for Computational Linguistics, 2018 BASE Show details 35 Tracking Typological Traits of Uralic Languages in Distributed Language Representations ... Bjerva, Johannes; Augenstein, Isabelle. - : arXiv, 2017 BASE Show details 36 Turing at SemEval-2017 task 8 : sequential approach to rumour stance classification with branch-LSTM Kochkina, Elena; Liakata, Maria; Augenstein, Isabelle. - : Association for Computational Linguistics, 2017 BASE Show details 37 Mining equivalent relations from Linked Data Augenstein, Isabelle; Zhang, Ziqi; Blomqvist, Eva. - : Association for Computational Linguistics, 2013 BASE Show details Page: 1 2

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