Papers by Deniz Ekin Yavas
On the Relation Between Fine-Tuning, Topological Properties, and Task Performance in Sense-Enhanced Embeddings (2025.acl-long)
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| Challenge: | Enhanced word embeddings do not align well with word senses, resulting in poor performance on word sense identification tasks. |
| Approach: | They propose to use two methods to fine-tune embeddings to identify the topological properties that contribute to sense-enhanced embeddables. |
| Outcome: | The proposed methods improve the embeddings’ ability to capture nuanced semantic distinctions while reducing their expressiveness. |
Improving Word Sense Induction through Adversarial Forgetting of Morphosyntactic Information (2024.starsem-1)
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| Challenge: | Contextualized word representations from pre-trained language models encode more information than is necessary for the identification of word senses and some of this information affect performance negatively in unsupervised settings. |
| Approach: | They propose to use a framework to erase specific information from pre-trained word models and create feature-invariant representations that are invariant to these ‘nuisance features’. |
| Outcome: | The proposed framework erases information from the representations of pre-trained language models, thereby creating feature-invariant representations. |