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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 – 6 of 6 1 Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ... Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 2 Investigating the cross-lingual translatability of VerbNet-style classification. ... Majewska, Olga; Vulić, Ivan; McCarthy, Diana. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 3 Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo; Naradowsky, Jason; Reichart, Roi; Korhonen, Anna-Leena. - : MIT Press - Journals, 2018. : Transactions of the Association for Computational Linguistics, 2018 Abstract: Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available. ; This work is supported by the ERC Consolidator Grant LEXICAL (648909) URL: https://www.repository.cam.ac.uk/handle/1810/279936 https://doi.org/10.17863/CAM.27304 BASE Hide details 4 Investigating the cross-lingual translatability of VerbNet-style classification. Majewska, Olga; Vulić, Ivan; McCarthy, Diana. - : Springer Science and Business Media LLC, 2018. : Lang Resour Eval, 2018 BASE Show details 5 Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine. Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan. - : BioMed Central, 2018. : BMC bioinformatics, 2018 BASE Show details 6 Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine. Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018 BASE Show details

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