Search Results for author: Matthias Paulik

Found 8 papers, 3 papers with code

Consistent Transcription and Translation of Speech

1 code implementation24 Jul 2020 Matthias Sperber, Hendra Setiawan, Christian Gollan, Udhyakumar Nallasamy, Matthias Paulik

To address various shortcomings of this paradigm, recent work explores end-to-end trainable direct models that translate without transcribing.

speech-recognition Speech Recognition +1

Variational Neural Machine Translation with Normalizing Flows

no code implementations ACL 2020 Hendra Setiawan, Matthias Sperber, Udhay Nallasamy, Matthias Paulik

Variational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on some latent random variables.

Machine Translation NMT +2

Speech Translation and the End-to-End Promise: Taking Stock of Where We Are

no code implementations ACL 2020 Matthias Sperber, Matthias Paulik

Over its three decade history, speech translation has experienced several shifts in its primary research themes; moving from loosely coupled cascades of speech recognition and machine translation, to exploring questions of tight coupling, and finally to end-to-end models that have recently attracted much attention.

Machine Translation speech-recognition +2

Empirical Evaluation of Active Learning Techniques for Neural MT

no code implementations WS 2019 Xiangkai Zeng, Sarthak Garg, Rajen Chatterjee, Udhyakumar Nallasamy, Matthias Paulik

Finally, we propose a neural extension for an AL sampling method used in the context of phrase-based MT - Round Trip Translation Likelihood (RTTL).

Active Learning Machine Translation +3

Jointly Learning to Align and Translate with Transformer Models

1 code implementation IJCNLP 2019 Sarthak Garg, Stephan Peitz, Udhyakumar Nallasamy, Matthias Paulik

The state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches.

Machine Translation Translation +1

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