3 citations · 6 across the 7 of their papers we have counts for
7 papers
Representation Collapse in Machine Translation Through the Lens of Angular Dispersion
Evgeniia Tokarchuk, Maya K. Nachesa, Sergey Troshin +1
Modern neural translation models based on the Transformer architecture are known for their high performance, particularly when trained on high-resource datasets. A standard next-to…
Angular Dispersion Accelerates -Nearest Neighbors Machine Translation
Evgeniia Tokarchuk, Sergey Troshin, Vlad Niculae
Augmenting neural machine translation with external memory at decoding time, in the form of k-nearest neighbors machine translation (-NN MT), is a well-established strategy for…
Keep your distance: learning dispersed embeddings on
Evgeniia Tokarchuk, Hua Chang Bakker, Vlad Niculae
Learning well-separated features in high-dimensional spaces, such as text or image embeddings, is crucial for many machine learning applications. Achieving such separation can be e…
The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation
Evgeniia Tokarchuk, Vlad Niculae
Continuous-output neural machine translation (CoNMT) replaces the discrete next-word prediction problem with an embedding prediction. The semantic structure of the target embedding…
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition
Evgeniia Tokarchuk, David Thulke, Weiyue Wang +2
Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of sample…
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer
Evgeniia Tokarchuk, Jan Rosendahl, Weiyue Wang +4
Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs. It benefits from using high res…
Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformer
Evgeniia Tokarchuk, Jan Rosendahl, Weiyue Wang +4
Complex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models. However, cascaded models are known to be prone to error…