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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 Multilingual and Cross-Lingual Intent Detection from Spoken Data ... Gerz, Daniela; Su, Pei-Hao; Kusztos, Razvan. - : arXiv, 2021 BASE Show details 2 Multilingual and Cross-Lingual Intent Detection from Spoken Data ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gerz, Daniela; Kusztos, Razvan. - : Underline Science Inc., 2021 BASE Show details 3 Scoring Lexical Entailment with a Supervised Directional Similarity Network ... Rei, Marek; Gerz, Daniela; Vulić, Ivan. - : arXiv, 2018 BASE Show details 4 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. - : Apollo - University of Cambridge Repository, 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 ... : This work is supported by the ERC Consolidator Grant LEXICAL (648909) ... URL: https://dx.doi.org/10.17863/cam.27304 https://www.repository.cam.ac.uk/handle/1810/279936 BASE Hide details 5 Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo. - : MIT Press - Journals, 2018. : Transactions of the Association for Computational Linguistics, 2018 BASE Show details 6 HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment ... Vulić, Ivan; Gerz, Daniela; Kiela, Douwe. - : arXiv, 2016 BASE Show details

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