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首页> 外文会议>Workshop on multilingual and cross-lingual methods in NLP 2016 >Learning Translations for Tagged Words: Extending the Translation Lexicon of an ITG for Low Resource Languages
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Learning Translations for Tagged Words: Extending the Translation Lexicon of an ITG for Low Resource Languages

机译：学习标记词的翻译：扩展ITG的翻译词典，以实现低资源语言

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摘要

We tackle the challenge of learning part-of-speech classified translations as part of an inversion transduction grammar, by learning translations for English words with known part-of-speech tags, both from existing translation lexica and from parallel corpora. When translating from a low resource language into English, we can expect to have rich resources for English, such as treebanks, and small amounts of bilingual resources, such as translation lexica and parallel corpora. We solve the problem of integrating these heterogeneous resources into a single model using stochastic Inversion Transduction Grammars, which we augment with wildcards to handle unknown translations.
机译：我们通过从现有的翻译词典和并行语料库中学习具有已知词性标签的英语单词的翻译，来解决将词性分类翻译作为反向翻译语法一部分学习的挑战。从低资源语言翻译成英语时，我们可以期望拥有丰富的英语资源（例如树库）和少量的双语资源（例如翻译词典和并行语料库）。我们使用随机的Inversion Transduction Grammrs解决了将这些异构资源集成到单个模型中的问题，我们使用通配符对其进行了扩充以处理未知翻译。

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  • 会议地点 San Diego CA(US)
  • 作者

    Markus Saers; Dekai Wu;

  • 作者单位

    Human Language Technology Center Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKUST, Clear Water Bay, Kowloon, Hong Kong;

    Human Language Technology Center Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKUST, Clear Water Bay, Kowloon, Hong Kong;

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  • 正文语种 eng
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