Papers by Elisabetta Fersini
Steering Large Language Models for Machine Translation Personalization (2026.eacl-long)
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| Challenge: | Recent advances in interpretability research have highlighted the effectiveness of steering methods for MT personalization. |
| Approach: | They examine steering strategies for personalizing automatic translations when few examples are available. |
| Outcome: | The proposed steering methods yield higher inference-time computational efficiency than prompting approaches. |
Exploring Neural Topic Modeling on a Classical Latin Corpus (2024.lrec-main)
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| Challenge: | Using topic modeling, it is possible to study Latin literature through methods and tools that support distant reading. |
| Approach: | They propose to use topic modeling to investigate thematic distribution of Latin corpus . they train, optimize and compare two neural models to evaluate which performs better . |
| Outcome: | The proposed model is compared with two neural models with a Classical Latin corpus and shows that it is coherent and interpretable. |
Cross-lingual Contextualized Topic Models with Zero-shot Learning (2021.eacl-main)
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| Challenge: | Existing topic models are language-specific and cannot be transferred in a transferable manner. |
| Approach: | They propose a zero-shot cross-lingual topic model that learns topics on one language and predicts them for unseen documents in different languages. |
| Outcome: | The proposed model learns topics on one language and predicts them for unseen documents in different languages. |
OCTIS: Comparing and Optimizing Topic models is Simple! (2021.eacl-demos)
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| Challenge: | Current topic modeling frameworks focus on preprocessing, evaluation, comparison of models and visualization. |
| Approach: | They propose an evaluation framework for Topic Models with optimal hyper-parameters estimated using Bayesian Optimization approach. |
| Outcome: | The proposed framework integrates several state-of-the-art topic models and evaluation metrics. |