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» Learning Rules to Improve a Machine Translation System
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ICML
2001
IEEE
16 years 18 days 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 1 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
14 years 9 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 3 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 3 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