Papers by Matej Ulčar
Multilingual Culture-Independent Word Analogy Datasets (2020.lrec-1)
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| Challenge: | In text processing, deep neural networks use word embeddings as an input. |
| Approach: | They propose to use benchmark datasets to compare the quality of word embeddings in text processing . they use a word analogy task in Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian, and Swedish . |
| Outcome: | The proposed datasets are culturally independent and cross-lingual for the languages used. |
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context (2020.lrec-1)
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Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, Mark Granroth-Wilding
| Challenge: | Existing methods to evaluate word embeddings ignore context and treat words in isolation. |
| Approach: | They propose to build a new word embeddings-based dataset that provides context-dependent similarity measures. |
| Outcome: | The proposed dataset provides context-dependent similarity measures and covers a well-resourced language (English) but a number of less-resource languages. |
High Quality ELMo Embeddings for Seven Less-Resourced Languages (2020.lrec-1)
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| Challenge: | Recent results show that deep neural networks using contextual embeddings outperform non-contextual embedders on a majority of text classification tasks. |
| Approach: | They propose to use contextual embeddings for seven languages to train new embeddables . they also show that existing embeddibles for listed languages shall be improved . |
| Outcome: | The proposed embeddings outperform non-contextual embeddables on a majority of text classification tasks. |