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NAACL
2010
14 years 7 months ago
Model Combination for Machine Translation
Machine translation benefits from two types of decoding techniques: consensus decoding over multiple hypotheses under a single model and system combination over hypotheses from di...
John DeNero, Shankar Kumar, Ciprian Chelba, Franz ...
92
Voted
EMNLP
2008
14 years 11 months ago
Lattice-based Minimum Error Rate Training for Statistical Machine Translation
Minimum Error Rate Training (MERT) is an effective means to estimate the feature function weights of a linear model such that an automated evaluation criterion for measuring syste...
Wolfgang Macherey, Franz Josef Och, Ignacio Thayer...
ACL
2012
13 years 14 hour ago
Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization Evaluation
An ideal summarization system should produce summaries that have high content coverage and linguistic quality. Many state-ofthe-art summarization systems focus on content coverage...
Ziheng Lin, Chang Liu, Hwee Tou Ng, Min-Yen Kan
79
Voted
ACL
2008
14 years 11 months ago
Combining Source and Target Language Information for Name Tagging of Machine Translation Output
A Named Entity Recognizer (NER) generally has worse performance on machine translated text, because of the poor syntax of the MT output and other errors in the translation. As som...
Shasha Liao
ACL
2011
14 years 1 months ago
Combining Morpheme-based Machine Translation with Post-processing Morpheme Prediction
This paper extends the training and tuning regime for phrase-based statistical machine translation to obtain fluent translations into morphologically complex languages (we build ...
Ann Clifton, Anoop Sarkar