The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10130040" target="_blank" >RIV/00216208:11320/12:10130040 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation

Popis výsledku v původním jazyce

We describe the "Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation" (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT). Participants of the challenge are requested to build hybrid translations by combining the output of several MT systems of different types. We ?rst describe the ML4HMT corpus used in the shared task, then explain the XLIFF-based annotation format we have designedfor it, and brie?y summarize the participating systems. Using both automated metrics scores and extensive manual evaluation, we discuss the individual performance of the various systems. An interesting result from the shared task is the fact that we were able to observe different systems winning according to the automated metrics scores when compared to the results from the manual evaluation. We conclude by summarising the ?rst edition of the challenge and by giving an outlook to future

Název v anglickém jazyce

The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation

Popis výsledku anglicky

We describe the "Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation" (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT). Participants of the challenge are requested to build hybrid translations by combining the output of several MT systems of different types. We ?rst describe the ML4HMT corpus used in the shared task, then explain the XLIFF-based annotation format we have designedfor it, and brie?y summarize the participating systems. Using both automated metrics scores and extensive manual evaluation, we discuss the individual performance of the various systems. An interesting result from the shared task is the fact that we were able to observe different systems winning according to the automated metrics scores when compared to the results from the manual evaluation. We conclude by summarising the ?rst edition of the challenge and by giving an outlook to future

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)

ISBN

978-2-9517408-7-7

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

3430-3435

Název nakladatele

European Language Resources Association

Místo vydání

?stanbul, Turkey

Místo konání akce

?stanbul, Turkey

Datum konání akce

21. 5. 2012

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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