ACL RD-TEC 1.0 Summarization of P04-1021

Paper Title:
A JOINT SOURCE-CHANNEL MODEL FOR MACHINE TRANSLITERATION

Authors: Haizhou Li and Min Zhang and Jian Su

Other assigned terms:

  • alphabet
  • approach
  • back-transliteration
  • backoff
  • bigram
  • bilingual dictionary
  • case
  • character error rate
  • character sequence
  • characters
  • chinese characters
  • conditional probability
  • contextual information
  • data set
  • data sets
  • derivation
  • dictionary
  • distribution
  • dom framework
  • english-chinese language pair
  • error rate
  • exact match
  • french
  • implementation
  • interpretation
  • joint probability
  • joint probability model
  • katakana
  • knowledge
  • knowledge base
  • language models
  • language pair
  • language pairs
  • likelihood
  • linguistics
  • mapping
  • mappings
  • meaning
  • meanings
  • method
  • multilingual corpus
  • n-gram
  • n-gram model
  • n-gram models
  • n-gram transliteration model
  • names
  • noisy channel
  • open test
  • orthography
  • perplexity
  • personal names
  • phonemes
  • phonemic representation
  • pinyin
  • precision
  • probability
  • probability distribution
  • probability distributions
  • probability model
  • process
  • proper names
  • russian
  • source language
  • source-channel model
  • speech synthesis literature
  • statistics
  • substring
  • system development
  • target language
  • target languages
  • test data
  • test set
  • tokens
  • training
  • training data
  • training data set
  • training database
  • transformation
  • transformation rules
  • transliteration model
  • tree
  • tree path
  • trees
  • trigram
  • word
  • word error rates
  • words

Extracted Section Types:


This page last edited on 10 May 2017.

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