Papers with transformer-based methods
Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging (2020.findings-emnlp)
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| Challenge: | Ezafe is a grammatical particle in some Iranian languages that links two words together but is almost always not indicated in Persian script. |
| Approach: | They propose to use Persian ezafe to improve part-of-speech tagging by using transformer-based methods to achieve state-of the-art results. |
| Outcome: | The proposed methods achieve state-of-the-art in the task of ezafe recognition and show that they are not useful to transformer-based methods. |
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models (2023.eacl-main)
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Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann
| Challenge: | Large multilingual models have inspired a new class of word alignment methods, which work well for pretraining languages. |
| Approach: | They propose to use transformer-based word alignment methods to extract alignments from massive pretrained models. |
| Outcome: | The proposed methods outperform traditional methods for languages unseen to pretraining models, and are competitive with each other. |
Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation (2023.emnlp-main)
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| Challenge: | Visual word sense disambiguation (VWSD) is a challenging task involving multiple candidates . context given for an ambiguous word is minimal, most often limited to a single word . |
| Approach: | They propose to use large language models to enhance given phrases and resolve ambiguity related to the target word. |
| Outcome: | The proposed frameworks improve the image representation of ambiguous words among candidates and achieve competitive ranking results. |