An iterative, DP-based search algorithm for statistical machine translation
An iterative, DP-based search algorithm for statistical machine translation
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用于统计机器翻译的基于 DP 的迭代搜索算法
DOI：
10.21437/icslp.1998-567
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
H. Ney
中科院分区：
文献类型：
--
作者：
I. García;F. Casacuberta;H. Ney
对机器翻译的统计方法的兴趣越来越大，这是由于迄今为止提出的用于训练概率模型的有效算法的发展。然而，统计机器翻译的一个开放问题是设计有效的算法来翻译给定的输入字符串。对于一些有趣的模型，只能找到（好的）近似解，最近引入了一种类似动态规划的算法来计算一些模型的近似解。这些解决方案可以通过使用迭代算法来改进，该迭代算法细化了迭代解决方案，并基于不同分布的插值对模型的某些概率分布使用平滑技术。从这种组合产生的技术已经过测试的“旅游任务”语料库，这是在一个半自动化的方式产生的。取得的最好结果是9.3%的单词错误率和44.4%的重复错误率。
The increasing interest in the statistical approach to Machine Translation is due to the development of effective algorithms for training the probabilistic models proposed so far. However, one of the open problems with Statistical Machine Translation is the design of efficient algorithms for translating a given input string. For some interesting models, only (good) approximate solutions can be found. Recently a Dynamic Programming-like algorithm has been introduced which computes approximate solutions for some models. These solutions can be improved by using an iterative algorithm that refines the succesive solutions and uses a smoothing technique for some probabilistic distribution of the models based on an interpolation of different distributions. The technique resulting from this combination has been tested on the “Tourist Task” corpus, which was generated in a semi-automated way. The best results achieved were a word-error rate of 9.3% and a sentence-error rate of 44.4%.