The IWSLT 2021 BUT Speech Translation Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU144029" target="_blank" >RIV/00216305:26230/21:PU144029 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2021.iwslt-1.7.pdf" target="_blank" >https://aclanthology.org/2021.iwslt-1.7.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2021.iwslt-1.7" target="_blank" >10.18653/v1/2021.iwslt-1.7</a>

Result language

angličtina

Original language name

The IWSLT 2021 BUT Speech Translation Systems

Original language description

The paper describes BUTs English to German offline speech translation (ST) systems developed for IWSLT2021. They are based on jointly trained Automatic Speech Recognition- Machine Translation models. Their performances is evaluated on MustC-Common test set. In this work, we study their efficiency from the perspective of having a large amount of separate ASR training data and MT training data, and a smaller amount of speechtranslation training data. Large amounts of ASR and MT training data are utilized for pretraining the ASR and MT models. Speechtranslation data is used to jointly optimize ASR-MT models by defining an end-to-end differentiable path from speech to translations. For this purpose, we use the internal continuous representations from the ASR-decoder as the input to MT module. We show that speech translation can be further improved by training the ASR-decoder jointly with the MT-module using large amount of text-only MT training data. We also show significant improvements by training an ASR module capable of generating punctuated text, rather than leaving the punctuation task to the MT module.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 18th International Conference on Spoken Language Translation (IWSLT)

ISBN

978-1-7138-3378-9

ISSN

—

e-ISSN

—

Number of pages

9

Pages from-to

75-83

Publisher name

Association for Computational Linguistics

Place of publication

Bangkok, on-line

Event location

Bangkok (on-line)

Event date

Aug 5, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000694723100007