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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 Hits 1 – 18 of 18 1 Inducing Language-Agnostic Multilingual Representations ... Zhao, Wei; Eger, Steffen; Bjerva, Johannes. - : arXiv, 2020 BASE Show details 2 Zero-Shot Cross-Lingual Transfer with Meta Learning ... Nooralahzadeh, Farhad; Bekoulis, Giannis; Bjerva, Johannes. - : arXiv, 2020 BASE Show details 3 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 4 Uncovering Probabilistic Implications in Typological Knowledge Bases ... Bjerva, Johannes; Kementchedjhieva, Yova; Cotterell, Ryan. - : arXiv, 2019 BASE Show details 5 Back to the Future -- Sequential Alignment of Text Representations ... Bjerva, Johannes; Kouw, Wouter; Augenstein, Isabelle. - : arXiv, 2019 BASE Show details 6 What do Language Representations Really Represent? ... Bjerva, Johannes; Östling, Robert; Veiga, Maria Han. - : arXiv, 2019 BASE Show details 7 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 8 Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding ... Kementchedjhieva, Yova; Bjerva, Johannes; Augenstein, Isabelle. - : arXiv, 2018 BASE Show details 9 Parameter sharing between dependency parsers for related languages ... de Lhoneux, Miryam; Bjerva, Johannes; Augenstein, Isabelle. - : arXiv, 2018 BASE Show details 10 Multitask and Multilingual Modelling for Lexical Analysis ... Bjerva, Johannes. - : arXiv, 2018 BASE Show details 11 From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings ... Bjerva, Johannes; Augenstein, Isabelle. - : arXiv, 2018 BASE Show details 12 The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations Abzianidze, Lasha; Bjerva, Johannes; Evang, Kilian... In: 15th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01630960 ; 15th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2017, Valencia, Spain. pp.242 - 247 (2017) BASE Show details 13 Tracking Typological Traits of Uralic Languages in Distributed Language Representations ... Bjerva, Johannes; Augenstein, Isabelle. - : arXiv, 2017 BASE Show details 14 Articulation rate in Swedish child-directed speech increases as a function of the age of the child even when surprisal is controlled for ... Sjons, Johan; Hörberg, Thomas; Östling, Robert. - : arXiv, 2017 BASE Show details 15 One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis ... Bjerva, Johannes. - : arXiv, 2017 BASE Show details 16 The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations ... Abzianidze, Lasha; Bjerva, Johannes; Evang, Kilian. - : arXiv, 2017 BASE Show details 17 Rethinking intertextuality through a word-space and social network approach – the case of Cassiodorus Bjerva, Johannes; Praet, Raf In: https://hal.archives-ouvertes.fr/hal-01279833 ; 2016 (2016) BASE Show details 18 Semantic Tagging with Deep Residual Networks ... Bjerva, Johannes; Plank, Barbara; Bos, Johan. - : arXiv, 2016 BASE Show details

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