2 code implementations • Findings (ACL) 2022 • Orion Weller, Matthias Sperber, Telmo Pires, Hendra Setiawan, Christian Gollan, Dominic Telaar, Matthias Paulik
Code switching (CS) refers to the phenomenon of interchangeably using words and phrases from different languages.
no code implementations • 16 Feb 2021 • Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier Van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, Sudeep Agarwal, Julien Freudiger, Andrew Byde, Abhishek Bhowmick, Gaurav Kapoor, Si Beaumont, Áine Cahill, Dominic Hughes, Omid Javidbakht, Fei Dong, Rehan Rishi, Stanley Hung
We describe the design of our federated task processing system.
no code implementations • 6 Aug 2020 • Filip Granqvist, Matt Seigel, Rogier Van Dalen, Áine Cahill, Stephen Shum, Matthias Paulik
From these features, the model predicts speaker characteristic labels considered useful as side information.
1 code implementation • 24 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.
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.
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.
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).
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.