Papers by Pavel Petrushkov
Learning from Chunk-based Feedback in Neural Machine Translation (P18-2)
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| Challenge: | a common problem with explicit ratings of translations is that users are not qualified enough to provide reliable feedback for the whole sentence. |
| Approach: | They propose a way to learn from partial feedback in neural machine translation . they ask users to highlight a correct chunk of a translation based on partial feedback . |
| Outcome: | The proposed method outperforms sentence-based feedback by 2.61% BLEU absolute. |
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages (D19-1)
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| Challenge: | Using parallel corpora, we train a single, direct NMT model for non-English language pairs. |
| Approach: | They propose three ways to increase the relation among source, pivot, and target languages in pre-training . they use additional adapter component to smoothly connect pre-trained encoder and decoder . |
| Outcome: | The proposed methods outperform multilingual models up to +2.6% BLEU in WMT 2019 French-German and German-Czech tasks. |
Domain Adaptation of Foundation LLMs for e-Commerce (2025.acl-industry)
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Christian Herold, Michael Kozielski, Tala Bazazo, Pavel Petrushkov, Yannick Versley, Seyyed Hadi Hashemi, Patrycja Cieplicka, Dominika Basaj, Shahram Khadivi
| Challenge: | Large Language Models (LLMs) have greatly improved the performance on most natural language tasks, and often show surprisingly good zero-shot generalization to new domains. |
| Approach: | They propose to continuously pretrain the Llama 3.1 base models on 1 trillion tokens of e-commerce data to introduce domain specific knowledge into the model while at the same time keeping the general capabilities intact. |
| Outcome: | The proposed model can be adapted to the new domain without sacrificing performance on general domain tasks. |