@article{lopez-etal-2013-learning,
title = "Learning to translate with products of novices: a suite of open-ended challenge problems for teaching {MT}",
author = "Lopez, Adam and
Post, Matt and
Callison-Burch, Chris and
Weese, Jonathan and
Ganitkevitch, Juri and
Ahmidi, Narges and
Buzek, Olivia and
Hanson, Leah and
Jamil, Beenish and
Lee, Matthias and
Lin, Ya-Ting and
Pao, Henry and
Rivera, Fatima and
Shahriyari, Leili and
Sinha, Debu and
Teichert, Adam and
Wampler, Stephen and
Weinberger, Michael and
Xu, Daguang and
Yang, Lin and
Zhao, Shang",
editor = "Lin, Dekang and
Collins, Michael",
journal = "Transactions of the Association for Computational Linguistics",
volume = "1",
year = "2013",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q13-1014",
doi = "10.1162/tacl_a_00218",
pages = "165--178",
abstract = "Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-of-the-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.",
}
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<abstract>Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-of-the-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.</abstract>
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%0 Journal Article
%T Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT
%A Lopez, Adam
%A Post, Matt
%A Callison-Burch, Chris
%A Weese, Jonathan
%A Ganitkevitch, Juri
%A Ahmidi, Narges
%A Buzek, Olivia
%A Hanson, Leah
%A Jamil, Beenish
%A Lee, Matthias
%A Lin, Ya-Ting
%A Pao, Henry
%A Rivera, Fatima
%A Shahriyari, Leili
%A Sinha, Debu
%A Teichert, Adam
%A Wampler, Stephen
%A Weinberger, Michael
%A Xu, Daguang
%A Yang, Lin
%A Zhao, Shang
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F lopez-etal-2013-learning
%X Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-of-the-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.
%R 10.1162/tacl_a_00218
%U https://aclanthology.org/Q13-1014
%U https://doi.org/10.1162/tacl_a_00218
%P 165-178
Markdown (Informal)
[Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT](https://aclanthology.org/Q13-1014) (Lopez et al., TACL 2013)
ACL
- Adam Lopez, Matt Post, Chris Callison-Burch, Jonathan Weese, Juri Ganitkevitch, Narges Ahmidi, Olivia Buzek, Leah Hanson, Beenish Jamil, Matthias Lee, Ya-Ting Lin, Henry Pao, Fatima Rivera, Leili Shahriyari, Debu Sinha, Adam Teichert, Stephen Wampler, Michael Weinberger, Daguang Xu, et al.. 2013. Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT. Transactions of the Association for Computational Linguistics, 1:165–178.