IWSLT Task

task ID: iwslttask

Related Papers

Minimum Imputed-Risk: Unsupervised Discriminative Training for Machine Translation

Authors: nan

Publication: TIPSTER (2011)

DOI: nan

Discriminative training for machine transla- tion has been well studied in the recent past. A limitation of the work to date is that it relies on the availability of high-quality …...

44257

Investigating the Usefulness of Generalized Word Representations in SMT

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

We investigate the use of generalized representations (POS, morphological analysis and word clusters) in phrase-based models and the N-gram-based Operation Sequence Model (OSM). Our integration enables these models to learn …...

6004

Samsung and University of Edinburgh’s System for the IWSLT 2018 Low Resource MT Task

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThis paper describes the joint submission to the IWSLT 2018 Low Resource MT task by Samsung R&D Institute, Poland, and the University of Edinburgh. We focused on supplementing the very …...

20620

Multiscale Collaborative Deep Models for Neural Machine Translation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractRecent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a MultiScale Collaborative …...

38381

Enriching the Transformer with Linguistic Factors for Low-Resource Machine Translation

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractIntroducing factors, that is to say, word features such as linguistic information referring to the source tokens, is known to improve the results of neural machine translation systems in certain …...

20360

NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper provides an overview of NVIDIA NeMo’s speech translation systems for the IWSLT 2022 Offline Speech Translation Task. Our cascade system consists of 1) Conformer RNN-T automatic speech recognition …...

20679