Robustness of Multi-Source MT to Transcription Errors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10475914" target="_blank" >RIV/00216208:11320/23:10475914 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2023.findings-acl.228.pdf" target="_blank" >https://aclanthology.org/2023.findings-acl.228.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Robustness of Multi-Source MT to Transcription Errors

Original language description

Automatic speech translation is sensitive to speech recognition errors, but in a multilingual scenario, the same content may be available in various languages via simultaneous interpreting, dubbing or subtitling. In this paper, we hypothesize that leveraging multiple sources will improve translation quality if the sources complement one another in terms of correct information they contain. To this end, we first show that on a 10-hour ESIC corpus, the ASR errors in the original English speech and its simultaneous interpreting into German and Czech are mutually independent. We then use two sources, English and German, in a multi-source setting for translation into Czech to establish its robustness to ASR errors. Furthermore, we observe this robustness when translating both noisy sources together in a simultaneous translation setting. Our results show that multi-source neural machine translation has the potential to be useful in a real-time simultaneous translation setting, thereby motivating further investigation in this area.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2023

Confidentiality

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

Article name in the collection

Findings of the Association for Computational Linguistics: ACL 2023

ISBN

978-1-959429-62-3

ISSN

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e-ISSN

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Number of pages

17

Pages from-to

3707-3723

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg, PA, USA

Event location

Toronto, Canada

Event date

Jul 9, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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