Research on system combination of machine translation based on Transformer

Influenced by its training corpus,the performance of different machine translation systems varies greatly.Aiming at achieving higher quality translations,system combination methods combine the translation results of multiple systems through statistical combination or neural network combination.This paper proposes a new multi-system translation combination method based on the Transformer architecture,which uses a multi-encoder to encode source sentences and the translation results of each system in order to realize encoder combination and decoder combination.The experimental verification on the Chinese-English translation task shows that this method has 1.2-2.35 more bilingual evaluation understudy(BLEU)points compared with the best single system results,0.71-3.12 more BLEU points compared with the statistical combination method,and 0.14-0.62 more BLEU points compared with the state-of-the-art neural network combination method.The experimental results demonstrate the effectiveness of the proposed system combination method based on Transformer.

基于多模态图像深度学习局部晚期鼻咽癌肿瘤靶体积自动勾画的研究

目的:随着人工智能的发展,深度学习在放疗肿瘤靶体积自动勾画中的应用日益受到关注。由于T3~4期鼻咽癌的肿瘤表型变异性较大,并且在CT图像上表现为较低的软组织对比度,从而导致勾画肿瘤靶体积的信心受限。本研究基于多模态图像深度学习对T3~4期鼻咽癌大体肿瘤体积(gross tumor volume of nasopharyngeal carcinoma,GTVnx)进行自动勾画,以期提高靶区勾画的精度和效率。方法:回顾性收集T3~4期鼻咽癌患者的CT、MRI(脂肪抑制T2加权和增强T1加权)和PET-CT图像。将多模态图像输入Multi-Encoder U-net进行多组模型训练和测试。采用dice相似系数(dice similarity coefficient,DSC)和HD95定量分析GTVnx的自动勾画结果。结果:对比不同策略模块的试验结果,显示‘Cross-Y’S Net多模态图像深度学习网络自动勾画测试结果最佳,DSC为0.665±0.045,HD95为5.17±3.34。结论:基于多模态图像CT、MRI和PET-CT提供的优势互补信息进行深度学习,在定位CT图像上完成鼻咽癌放疗肿瘤靶体积的自动勾画,有望提高靶区勾画的精度和效率,增强临床应用的信心。随着人工智能和影像学技术的不断发展,未来会更好地适应肿瘤放疗的需求。