利用上下文信息的统计机器翻译领域自适应

统计机器翻译系统用于翻译领域文本时,常常会遇到跨领域的问题 当待翻译文本与训练语料来自同一领域时,通常会得到较好的翻译效果;当领域差别较大时,翻译质量会明显下降。某个特定领域的双语平行语料是有限的,相对来说,领域混杂的平行语料和特定领域的单语文本更容易获得。该文充分利用这一特点,提出了一种包含领域信息的翻译概率计算模型,该模型联合使用混合领域双语和特定领域源语言单语进行机器翻译领域自适应。实验显示,自适应模型在IWSLT机器翻译评测3个测试集上均比Baseline有提高,证明了该文方法的有效性。

SMT Domain Adaptation Based on Monolingual Context Information

Domain adaptation problem will arise when statistical machine translation (SMT) system is used to translate domain-specific texts. When the texts to be translated and the training data come from the same domain, SMT system can achieve good performance. Otherwise, the translation quality will degrade dramatically. In general, domain-specific parallel corpus is limited, while domain-mixed parallel corpus and domain-specific monolingual corpus are easy to obtain. According to the fact, this paper proposed a new translation model which utilized domain-mixed parallel corpus and domain-specific monolingual corpus to improve the domain translation quality. Experiments show that the proposed method improves translation performance in three IWSLT evaluation tests significantly.
Key wordsstatistical machine translation; domain adaptation; context information