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» Learning Rules to Improve a Machine Translation System
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ICML
2001
IEEE
16 years 7 months ago
Round Robin Rule Learning
In this paper, we discuss a technique for handling multi-class problems with binary classifiers, namely to learn one classifier for each pair of classes. Although this idea is kno...
Johannes Fürnkranz
EMNLP
2008
15 years 8 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...
EMNLP
2009
15 years 4 months ago
Discriminative Corpus Weight Estimation for Machine Translation
Current statistical machine translation (SMT) systems are trained on sentencealigned and word-aligned parallel text collected from various sources. Translation model parameters ar...
Spyros Matsoukas, Antti-Veikko I. Rosti, Bing Zhan...
ACL
2011
14 years 10 months ago
A Large Scale Distributed Syntactic, Semantic and Lexical Language Model for Machine Translation
This paper presents an attempt at building a large scale distributed composite language model that simultaneously accounts for local word lexical information, mid-range sentence s...
Ming Tan, Wenli Zhou, Lei Zheng, Shaojun Wang
KDD
1997
ACM
154views Data Mining» more  KDD 1997»
15 years 10 months ago
Autonomous Discovery of Reliable Exception Rules
This paper presents an autonomous algorithm for discovering exception rules from data sets. An exception rule, which is defined as a deviational pattern to a well-known fact, exhi...
Einoshin Suzuki