Best papers on ACL and EMNLP

EMNLP2002 http://ufal.ms.mff.cuni.cz/~hajic/emnlp02/best.html
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Michael Collins.
Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms.
EMNLP 2002. (Received Best Paper Award.)
(This paper includes theorems and proofs which apply to the algorithms in the ACL 2002 papers.)
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Frank Keller1, Maria Lapata1, Olga Ourioupina2
University of Edinburgh, United Kingdom and 2University of Saarland, Germany
Using the Web to Overcome Data Sparseness
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EMNLP2003 http://people.csail.mit.edu/mcollins/emnlp03.all.html
Training Connectionist Models for the Structured Language Model
Peng Xu, Ahmad Emami and Frederick Jelinek

EMNLP2004
Ben Taskar, Dan Klein, Michael Collins, Daphne Koller, and Christopher Manning. Max-Margin Parsing. EMNLP 2004. (Received Best Paper Award.)

ACL2001
"Fast Decoding and Optimal Decoding for Machine Translation" (U. Germann, M. Jahr, K. Knight, D. Marcu, and K. Yamada), Proc. of the Conference of the Association for Computational Linguistics (ACL-2001). ACL Best Paper award.

ACL2002
Franz Josef Och, Hermann Ney.
2002
Discriminative Training and Maximum Entropy Models for Statistical Machine Translation.
In "ACL 2002: Proc. of the 40th Annual Meeting of the Association for Computational Linguistics" (best paper award), pp. 295-302, Philadelphia, PA, July 2002.

ACL2003
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Yukiko Nakano, Gabe Reinstein, Tom Stocky and Justine Cassell. Towards a Model of Face-to-Face Grounding. ACL
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Dan Klein,Best Paper Award, ACL 2003, for "Accurate Unlexicalized Parsing"
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ACL2004
The best paper prize was awarded to Diana McCarthy, Rob Koeling, Julie Weeds, & John Carroll for their paper "Finding Predominant Word Senses in Untagged Text". In this paper they develop a method for selecting the predominant sense of a word from a corpus without sense annotations, using unsupervised thesaurus extraction techniques. The paper was rated highly by the reviewers and PC on quality and innovativeness, and was selected also because it combines two important topics in our field: unsupervised learning and robust semantic analysis.

ACL2005
David Chiang, A Hierarchical Phrase-Based Model for Statistical Machine Translation. (Best paper award.) This paper takes the standard phrase-based MT model that is popular in our field (basically, translate a sentence by individually translating phrases and reordering them according to a complicated statistical model) and extends it to take into account hierarchy in phrases, so that you can learn things like “X ’s Y” -> “Y de X” in chinese, where X and Y are arbitrary phrases. This takes a step toward linguistic syntax for MT, which our group is working strongly on, but doesn’t require any linguists to sit down and write out grammars or parse sentences.