Machine Translation without Word Alignments

While the vast majority of statistical machine translation systems maintain an alignment of source and target words (or word groups), some work explored machine translation without such alignment.

MT Without Word Alignment is the main subject of 5 publications. 4 are discussed here.

Publications

While word alignment is generally assumed to be an essential element of statistical translation models, methods have been proposed that first generate a bag of words from the source bag of words and then apply an ordering model (Venkatapathy and Bangalore, 2007; Bangalore et al., 2007). Alignment-free word translation models may be used as a feature function in traditional models. Mauser et al. (2009) employs a maximum entropy model that predicts output words from all source words as an additional scoring function. Huck et al. (2011) confirm the effectiveness of the method.

Benchmarks

Discussion

Related Topics

New Publications

• Langlais, Phillippe (2013): Mapping Source to Target Strings without Alignment by Analogical Learning: A Case Study with Transliteration, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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  @InProceedings{langlais:2013:Short,
  author = {Langlais, Phillippe},
  title = {Mapping Source to Target Strings without Alignment by Analogical Learning: A Case Study with Transliteration},
  booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month = {August},
  address = {Sofia, Bulgaria},
  publisher = {Association for Computational Linguistics},
  pages = {684--689},
  url = {http://www.aclweb.org/anthology/P13-2120},
  year = 2013
  }
  Langlais (2013)