Papers by Theodorus Fransen
Cross-lingual Sentence Embedding using Multi-Task Learning (2021.emnlp-main)
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| Challenge: | Existing multilingual sentence embedding models require large parallel corpora to learn efficiently, limiting their scope. |
| Approach: | They propose a sentence embedding framework based on an unsupervised loss function . they capture semantic similarity and relatedness between sentences using a multi-task loss function. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on STS, BUCC and Tatoeba benchmarks and on a monolingual benchmark. |
Unsupervised Deep Language and Dialect Identification for Short Texts (2020.coling-main)
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| Challenge: | Existing methods for identifying closely related short texts are unsupervised . however, performance is poor for unsupervised methods for short texts . |
| Approach: | They propose a method which can learn sentence embeddings and cluster assignments from short texts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in supervised settings . it can learn sentence embeddings and cluster assignments from short texts . |
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment (2020.lrec-1)
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Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Andrea Bellandi, Irene Pisani, Thomas Troelsgård, Sussi Olsen, Simon Krek, Veronika Lipp, Tamás Váradi, László Simon, András Gyorffy, Carole Tiberius, Tanneke Schoonheim, Yifat Ben Moshe, Maya Rudich, Raya Abu Ahmad, Dorielle Lonke, Kira Kovalenko, Margit Langemets, Jelena Kallas, Oksana Dereza, Theodorus Fransen, David Cillessen, David Lindemann, Mikel Alonso, Ana Salgado, José Luis Sancho, Rafael-J. Ureña-Ruiz, Jordi Porta Zamorano, Kiril Simov, Petya Osenova, Zara Kancheva, Ivaylo Radev, Ranka Stanković, Andrej Perdih, Dejan Gabrovsek
| Challenge: | a new dataset aims to align monolingual dictionaries with a single sense level for 15 languages . this dataset covers a wide range of languages and resources . |
| Approach: | They propose to manually align monolingual dictionaries with possible semantic relationships . they use 15 languages to create a new baseline for the task of monolingual word sense alignment . |
| Outcome: | The proposed dataset covers 15 languages and covers the more challenging task of linking general-purpose language. |
MHE: Code-Mixed Corpora for Similar Language Identification (2022.lrec-1)
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| Challenge: | a new corpus of code-mixed data-sets is presented for similar language identification . the data-settings are based on a more-resourced minority language, Magahi . |
| Approach: | They propose a Magahi-Hindi-English code-mixed corpus for similar language identification . they discuss the complexity of the data-set and provide a few baselines . |
| Outcome: | The proposed corpus provides a language id at two levels: word and sentence. |
MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis (2024.lrec-main)
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| Challenge: | Sociolinguists and psychologists have been studying these variations in the lexicons and the language from the 50's . code-mixing is a popular method for understanding people's emotions and attitudes towards various subjects, but low-resourced languages often have a mix of scripts and languages. |
| Approach: | They introduce a new sentiment data, MaCMS, for Magahi-Hindi-English code-mixed language, where Magai is a less-resourced minority language. |
| Outcome: | The proposed dataset is the first Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. |
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages (2023.findings-emnlp)
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| Challenge: | Existing approaches to cognate detection focus on orthographic, phonetic or contextual models, which under-perform for most under-resourced languages. |
| Approach: | They propose a language-agnostic weakly-supervised deep cognate detection framework for under-resourced languages using morphological knowledge from closely related languages. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on cognate detection datasets across languages and can be extended to a wide range of languages from any language family. |