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1
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
Ponti, Edoardo; Glavaš, Goran; Majewska, Olga. - : Apollo - University of Cambridge Repository, 2020
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2
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2020
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3
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
Vulic, Ivan; Baker, Simon; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2020
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4
Probing Pretrained Language Models for Lexical Semantics ...
Vulic, Ivan; Ponti, Edoardo; Litschko, Robert. - : Apollo - University of Cambridge Repository, 2020
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5
The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ...
Dubossarsky, Haim; Vulic, Ivan; Reichart, Roi. - : Apollo - University of Cambridge Repository, 2020
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6
Spatial multi-arrangement for clustering and multi-way similarity dataset construction ...
Majewska, Olga; McCarthy, D; Van Den Bosch, J. - : Apollo - University of Cambridge Repository, 2020
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7
The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures
Dubossarsky, Haim; Vulic, Ivan; Reichart, Roi. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020
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8
Spatial multi-arrangement for clustering and multi-way similarity dataset construction
Majewska, Olga; McCarthy, D; van den Bosch, J. - : European Language Resources Association, 2020. : LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, 2020
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9
Probing Pretrained Language Models for Lexical Semantics
Vulic, Ivan; Ponti, Edoardo; Litschko, Robert. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020
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10
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment
Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.2, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020
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11
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
Liu, Qianchu; Korhonen, Anna-Leena; Majewska, Olga. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020
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12
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
Ponti, Edoardo; O'Horan, Helen; Berzak, Yevgeni. - : Apollo - University of Cambridge Repository, 2019
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13
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ...
Shareghi, Ehsan; Gerz, Daniela; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019
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14
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Reichart, Roi; Shutova, Ekaterina; Korhonen, Anna-Leena; Poibeau, Thierry; Berzak, Yevgeni; Vulic, Ivan; O'Horan, Helen; Ponti, Edoardo. - : MIT Press - Journals, 2019. : COMPUTATIONAL LINGUISTICS, 2019
Abstract: Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.
URL: https://doi.org/10.17863/CAM.43731
https://www.repository.cam.ac.uk/handle/1810/296683
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15
Bio-SimVerb ...
Chiu, Hon Wing; Pyysalo, Sampo; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2018
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16
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ...
Ponti, Edoardo; Reichart, Roi; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2018
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17
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
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18
Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ...
Vulic, Ivan; Korhonen, Anna-Leena; Linguist, Assoc Computat. - : Apollo - University of Cambridge Repository, 2018
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19
Investigating the cross-lingual translatability of VerbNet-style classification. ...
Majewska, Olga; Vulić, Ivan; McCarthy, Diana. - : Apollo - University of Cambridge Repository, 2018
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20
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
Vulic, Ivan; Glavaš, Goran; Mrkšić, Nikola. - : Apollo - University of Cambridge Repository, 2018
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