Papers by Giorgos Vernikos
Subword Mapping and Anchoring across Languages (2021.findings-emnlp)
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| Challenge: | State-of-the-art multilingual systems rely on shared vocabularies that cover all considered languages. |
| Approach: | They propose a method to construct bilingual subword vocabularies by mapping and anchoring subwords together over multiple languages. |
| Outcome: | The proposed method improves zero-shot transfer to an unseen language without task-specific data, but only by sharing subword embeddings. |
Don’t Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation (2024.acl-long)
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| Challenge: | Neural machine translation models estimate probabilities of target sentences given source sentences, but these estimates may not align with human judgments. |
| Approach: | They propose a method that synthesizes translations using a quality estimation metric . they compare it with beam search and recent reranking techniques . |
| Outcome: | The proposed method outperforms other methods in large language models and multilingual translation models. |
Small Language Models Improve Giants by Rewriting Their Outputs (2024.eacl-long)
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| Challenge: | despite impressive performance of large language models, they lag behind specialized models in various tasks. |
| Approach: | They propose a training model that can be integrated with different LLMs at inference to improve their performance without task-specific training. |
| Outcome: | The proposed model outperforms standard models on four natural language generation tasks. |
Active Learning by Acquiring Contrastive Examples (2021.emnlp-main)
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| Challenge: | Using uncertainty and diversity sampling, active learning acquisition functions select difficult and diverse data points from a pool of unlabeled data. |
| Approach: | They propose an active learning acquisition function that selects contrastive examples from unlabeled data. |
| Outcome: | The proposed approach performs better or equal to the best performing baseline on all tasks, on both in-domain and out-of-domain data. |
Domain Adversarial Fine-Tuning as an Effective Regularizer (2020.findings-emnlp)
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| Challenge: | Existing fine-tuning techniques can degrade general-domain representations . however, fine-timing can lead to catastrophic forgetting of knowledge . |
| Approach: | They propose a new regularization technique that complements the task-specific loss used during fine-tuning with an adversarial objective. |
| Outcome: | Empirical results show that AFTER improves performance on various natural language understanding tasks compared to standard fine-tuning. |