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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 1 – 20 of 30 1 Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification Nedumpozhimana, Vasudevan; Klubička, Filip; Kelleher, John D. In: Front Artif Intell (2022) BASE Show details 2 Semantic Relatedness and Taxonomic Word Embeddings ... Kacmajor, Magdalena; Kelleher, John D.; Klubicka, Filip. - : arXiv, 2020 BASE Show details 3 English WordNet Taxonomic Random Walk Pseudo-Corpora Klubicka, Filip; Maldonado, Alfredo; Mahalunkar, Abhijit... In: Conference papers (2020) BASE Show details 4 Language related issues for machine translation between closely related south Slavic languages Arcan, Mihael; Klubicka, Filip; Popovic, Maja. - : The COLING 2016 Organizing Committee, 2019 BASE Show details 5 Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance Klubicka, Filip; Mahalunkar, Abhijit; Maldonado, Alfredo... In: Conference papers (2019) BASE Show details 6 Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings Maldonado, Alfredo; Klubicka, Filip; Kelleher, John D. In: Articles (2019) BASE Show details 7 Training corpus hr500k 1.0 Ljubešić, Nikola; Agić, Željko; Klubička, Filip. - : Jožef Stefan Institute, 2018 BASE Show details 8 Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian ... Klubička, Filip; Toral, Antonio; Sánchez-Cartagena, Víctor M.. - : arXiv, 2018 BASE Show details 9 Is it worth it? Budget-related evaluation metrics for model selection ... Klubička, Filip; Salton, Giancarlo D.; Kelleher, John D.. - : arXiv, 2018 BASE Show details 10 Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian Sanchez-Cartagena, Victor Manuel; Toral, Antonio; Klubicka, Filip In: Articles (2018) BASE Show details 11 Is it worth it? Budget-related evaluation metrics for model selection Klubicka, Filip; Salton, Giancarlo; Kelleher, John D. In: Conference papers (2018) BASE Show details 12 hr500k – A Reference Training Corpus of Croatian. Erjavec, Tomaž; Ljubešić, Nikola; Klubicka, Filip... In: Conference papers (2018) BASE Show details 13 Croatian Twitter training corpus ReLDI-NormTag-hr 1.1 Ljubešić, Nikola; Farkaš, Daša; Klubička, Filip. - : Jožef Stefan Institute, 2017 BASE Show details 14 Serbian Twitter training corpus ReLDI-NormTag-sr 1.0 Ljubešić, Nikola; Farkaš, Daša; Klubička, Filip. - : Jožef Stefan Institute, 2017 BASE Show details 15 Croatian Twitter training corpus ReLDI-NormTag-hr 1.0 Ljubešić, Nikola; Farkaš, Daša; Klubička, Filip. - : Jožef Stefan Institute, 2017 BASE Show details 16 Serbian Twitter training corpus ReLDI-NormTag-sr 1.1 Ljubešić, Nikola; Farkaš, Daša; Klubička, Filip. - : Jožef Stefan Institute, 2017 BASE Show details 17 Fine-grained human evaluation of neural versus phrase-based machine translation ... Klubička, Filip; Toral, Antonio; Sánchez-Cartagena, Víctor M.. - : arXiv, 2017 BASE Show details 18 Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation Klubička Filip; Toral Antonio; Sánchez-Cartagena Víctor M. In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 121-132 (2017) (2017) Abstract: We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems’ outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%. Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doi.org/10.1515/pralin-2017-0014 https://doaj.org/article/b4e1fd45807c4747bcc465fbf853507b BASE Hide details 19 Serbian-English parallel corpus srenWaC 1.0 Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016 BASE Show details 20 Finnish-English parallel corpus fienWaC 1.0 Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016 BASE Show details Page: 1 2

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