Search Results for author: Ra{\'u}l V{\'a}zquez

Found 6 papers, 0 papers with code

On the differences between BERT and MT encoder spaces and how to address them in translation tasks

no code implementations ACL 2021 Ra{\'u}l V{\'a}zquez, Hande Celikkanat, Mathias Creutz, J{\"o}rg Tiedemann

Various studies show that pretrained language models such as BERT cannot straightforwardly replace encoders in neural machine translation despite their enormous success in other tasks.

Machine Translation NMT +1

The University of Helsinki Submission to the IWSLT2020 Offline SpeechTranslation Task

no code implementations WS 2020 Ra{\'u}l V{\'a}zquez, Mikko Aulamo, Umut Sulubacak, J{\"o}rg Tiedemann

This paper describes the University of Helsinki Language Technology group{'}s participation in the IWSLT 2020 offline speech translation task, addressing the translation of English audio into German text.

Transfer Learning Translation

A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation

no code implementations CL 2020 Ra{\'u}l V{\'a}zquez, Aless Raganato, ro, Mathias Creutz, J{\"o}rg Tiedemann

In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks.

Machine Translation Sentence +2

The University of Helsinki Submissions to the WMT19 Similar Language Translation Task

no code implementations WS 2019 Yves Scherrer, Ra{\'u}l V{\'a}zquez, Sami Virpioja

This paper describes the University of Helsinki Language Technology group{'}s participation in the WMT 2019 similar language translation task.

Machine Translation Segmentation +1

An Evaluation of Language-Agnostic Inner-Attention-Based Representations in Machine Translation

no code implementations WS 2019 Aless Raganato, ro, Ra{\'u}l V{\'a}zquez, Mathias Creutz, J{\"o}rg Tiedemann

In this paper, we explore a multilingual translation model with a cross-lingually shared layer that can be used as fixed-size sentence representation in different downstream tasks.

Machine Translation Sentence +1

The University of Helsinki Submission to the WMT19 Parallel Corpus Filtering Task

no code implementations WS 2019 Ra{\'u}l V{\'a}zquez, Umut Sulubacak, J{\"o}rg Tiedemann

This paper describes the University of Helsinki Language Technology group{'}s participation in the WMT 2019 parallel corpus filtering task.

General Classification Sentence

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