@inproceedings{och-ney-2001-machine,
title = "What can machine translation learn from speech recognition?",
author = "Och, Franz Josef and
Ney, Hermann",
editor = "Krauwer, Steven",
booktitle = "Workshop on MT2010: Towards a Road Map for MT",
month = sep # " 18-22",
year = "2001",
address = "Santiago de Compostela, Spain",
url = "https://aclanthology.org/2001.mtsummit-road.6",
abstract = "The performance of machine translation technology after 50 years of development leaves much to be desired. There is a high demand for well performing and cheap MT systems for many language pairs and domains, which automatically adapt to rapidly changing terminology. We argue that for successful MT systems it will be crucial to apply data-driven methods, especially statistical machine translation. In addition, it will be very important to establish common test environments. This includes the availability of large parallel training corpora, well defined test corpora and standardized evaluation criteria. Thereby research results can be compared and this will open the possibility for more competition in MT research.",
}
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<abstract>The performance of machine translation technology after 50 years of development leaves much to be desired. There is a high demand for well performing and cheap MT systems for many language pairs and domains, which automatically adapt to rapidly changing terminology. We argue that for successful MT systems it will be crucial to apply data-driven methods, especially statistical machine translation. In addition, it will be very important to establish common test environments. This includes the availability of large parallel training corpora, well defined test corpora and standardized evaluation criteria. Thereby research results can be compared and this will open the possibility for more competition in MT research.</abstract>
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%0 Conference Proceedings
%T What can machine translation learn from speech recognition?
%A Och, Franz Josef
%A Ney, Hermann
%Y Krauwer, Steven
%S Workshop on MT2010: Towards a Road Map for MT
%D 2001
%8 sep 18 22
%C Santiago de Compostela, Spain
%F och-ney-2001-machine
%X The performance of machine translation technology after 50 years of development leaves much to be desired. There is a high demand for well performing and cheap MT systems for many language pairs and domains, which automatically adapt to rapidly changing terminology. We argue that for successful MT systems it will be crucial to apply data-driven methods, especially statistical machine translation. In addition, it will be very important to establish common test environments. This includes the availability of large parallel training corpora, well defined test corpora and standardized evaluation criteria. Thereby research results can be compared and this will open the possibility for more competition in MT research.
%U https://aclanthology.org/2001.mtsummit-road.6
Markdown (Informal)
[What can machine translation learn from speech recognition?](https://aclanthology.org/2001.mtsummit-road.6) (Och & Ney, MTSummit 2001)
ACL