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» Improving Language Models by Clustering Training Sentences
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NAACL
2007
13 years 9 months ago
Source-Language Features and Maximum Correlation Training for Machine Translation Evaluation
We propose three new features for MT evaluation: source-sentence constrained n-gram precision, source-sentence reordering metrics, and discriminative unigram precision, as well as...
Ding Liu, Daniel Gildea
COLING
2008
13 years 9 months ago
Sentence Type Based Reordering Model for Statistical Machine Translation
Many reordering approaches have been proposed for the statistical machine translation (SMT) system. However, the information about the type of source sentence is ignored in the pr...
Jiajun Zhang, Chengqing Zong, Shoushan Li
ECIR
2003
Springer
13 years 9 months ago
Clustering and Visualization in a Multi-lingual Multi-document Summarization System
Abstract. To measure the similarity of words, sentences, and documents is one of the major issues in multi-lingual multi-document summarization. This paper presents five strategies...
Hsin-Hsi Chen, June-Jei Kuo, Tsei-Chun Su
EACL
2009
ACL Anthology
13 years 5 months ago
Improving Grammaticality in Statistical Sentence Generation: Introducing a Dependency Spanning Tree Algorithm with an Argument S
like text summarisation requires a means of producing novel summary sentences. In order to improve the grammaticality of the generated sentence, we model a global (sentence) level...
Stephen Wan, Mark Dras, Robert Dale, Cécile...
ICASSP
2011
IEEE
12 years 11 months ago
Distributed training of large scale exponential language models
Shrinkage-based exponential language models, such as the recently introduced Model M, have provided significant gains over a range of tasks [1]. Training such models requires a l...
Abhinav Sethy, Stanley F. Chen, Bhuvana Ramabhadra...