Papers by Oleksii Hrinchuk
Hierarchical Policy Optimization for Simultaneous Translation of Unbounded Speech (2026.acl-long)
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| Challenge: | Existing synthesis methods cannot guarantee data quality. |
| Approach: | They propose a hierarchical reward that balances translation quality and latency objectives by combining supervised fine-tuning data with supervised inputs. |
| Outcome: | The proposed model can reuse key-value caches across both modalities and eliminate redundant feature recomputation. |
Anticipating Future with Large Language Model for Simultaneous Machine Translation (2025.naacl-long)
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Siqi Ouyang, Oleksii Hrinchuk, Zhehuai Chen, Vitaly Lavrukhin, Jagadeesh Balam, Lei Li, Boris Ginsburg
| Challenge: | Existing methods only use the partial utterance that has already arrived at the input and the generated hypothesis. |
| Approach: | They propose to use a large language model to predict future source words and opportunistically translate without introducing too much risk. |
| Outcome: | The proposed method outperforms baselines on four language directions and achieves the best translation quality-latency trade-off by up to 5 BLEU points at the same latency. |
Tensorized Embedding Layers (2020.findings-emnlp)
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| Challenge: | Using the Tensor Train decomposition, embeddings layers occupy large portion of model weights, preventing their deployment in limited resource settings. |
| Approach: | They propose a method for parameterizing embedding layers based on the Tensor Train decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance. |
| Outcome: | The proposed method can be plugged into any model and trained end-to-end. |
Leveraging Synthetic Targets for Machine Translation (2023.findings-acl)
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| Challenge: | Using synthetic target data, training models on synthetic targets outperforms training on actual ground-truth data. |
| Approach: | They propose a recipe for training machine translation models on synthetic target data by leveraging a large pre-trained model. |
| Outcome: | The proposed model outperforms training on real-world translation datasets. |