面向汉维机器翻译的调序表重构模型

Reordering table reconstruction model for Chinese-Uyghur machine translation

针对词汇化调序模型在机器翻译中存在的上下文无关性及稀疏性问题,提出了基于语义内容进行调序方向及概率预测的调序表重构模型。首先,使用连续分布式表示方法获取调序规则的特征向量;然后,通过循环神经网络(RNN)对于向量化表示的调序规则进行调序方向及概率预测;最后,过滤并重构调序表,赋予原始调序规则更加合理的调序概率分布值,提高调序模型中调序信息的准确度,同时降低调序表规模,提高后续解码速率。实验结果表明,将调序表重构模型应用至汉维机器翻译任务中,BLEU值可以获得0.39的提升。

Focused on the issue that lexicalized reordering models are faced with context independence and sparsity problems in machine translation, a reordering table reconstruction model based on semantic content for reordering orientation and probability prediction was proposed. Firstly, continuous distributed representation approach was employed to acquire the feature vectors of reordering rules. Secondly, Recurrent Neural Networks（ RNN） were utilized to predict the reordering orientation and probability of each reordering rule that represented with dense vectors. Finally, the original reordering table was filtered and reconstructed with more reasonable reordering probability distribution for the purpose of improving the reordering information accuracy in reordering model as well as reducing the size of the reordering table to speed up subsequent decoding process. The experimental results show that the reordering table reconstruction model can provide BLEU point gains（ ＋ 0. 39） for Chinese to Uyghur machine translation task.