Papers by Evgeniia Tokarchuk
Representation Collapse in Machine Translation Through the Lens of Angular Dispersion (2026.findings-eacl)
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| Challenge: | Effective training of Transformer models for sequential language tasks is difficult due to various forms of collapse of the internal representations learned. |
| Approach: | They propose to use angular dispersion to analyze representation collapse at different levels of discrete and continuous transformers throughout training. |
| Outcome: | The proposed method mitigates collapse and improves translation quality. |
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition (2021.acl-srw)
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| Challenge: | Existing methods for named entity recognition use only a limited number of samples . data augmentation and selftraining are popular methods to generate additional synthetic data . |
| Approach: | They investigate the impact of data augmentation and data augmented on named entity recognition tasks. |
| Outcome: | The proposed methods improve the performance of three named entity recognition tasks. |
Angular Dispersion Accelerates k-Nearest Neighbors Machine Translation (2025.findings-emnlp)
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| Challenge: | k-nearest neighbors machine translation is a well-established strategy for increasing translation performance . high computational cost and memory requirements make it difficult to perform exhaustive searches in large data stores . |
| Approach: | They propose to augment neural machine translation with external memory at decoding time . they propose to encourage angular dispersion of the neural hidden representations of contexts . |
| Outcome: | The proposed method is expensive and high memory-intensive . it is based on the performance properties of k-NN lookup data structures. |
The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation (2024.naacl-short)
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| Challenge: | Continuous-output neural machine translation models are trained to predict the continuous representation based on distances between vectors. |
| Approach: | They propose a continuous-output neural machine translation (CoNMT) approach that uses random output embeddings to outperform laboriously pre-trained models. |
| Outcome: | The proposed strategy outperforms pre-trained embeddings on large datasets and is strongest for rare words due to the geometry of their embedders. |