7-2 等长机器翻译研究及配音字幕的优化.pdf

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1、Isometric Machine Translation for Subtitling等长机器翻译及配音字幕优化杨浩华为-文本机器翻译实验室 主任Hao Yang,Director of Text Machine Translation Laboratories,Huawei|01NMT Basics&Trends02What Is Isometric MT03Isometric MT Architecture04Isometric MT Application目录Content|01NMT Trends|There is a white dog on the grass.草地上有只白色小狗

2、。f(x)convert sentenceFrom some languagesInto another languageMT Problem-机器翻译，肖桐，朱靖波等，2021SMT ProblemTranslation modelLanguage modelCompute argmaxNMT ProblemEnd-to-End modelNo feature extraction layerNo fine tune layerEncoder-Decoder ModelEncoder modelDecoder modelEncoded vectorHow to configure s5700

3、 arp?Source sentenceC如何如何怎么配置设置S5700S2700?arpap如何配置配置S5700S5700arparp?Target sentenceEncoder-Decoder ModelEncoder：RNNHidden state:encoded vectorDecoder:RNN1.3.1 Seq2Seq with Neural NetworksDecodingGreedy decodingBeam searchSamplingGreedy DecodingAt each step,keep several best hypothesesBeam SearchDe

4、codingSMT VS NMT=33.3 VS 34.81SMT Re-ranking=33.3 VS 36.6How to configure S5700 arp?如何 配置 s5700 arp?如何 配置 s5700 Seq2Seq Model ProblemContext vector is bottleneckPerformance degrades as sentence becomes longerHow to configure s5700 arp?Source sentenceC如何配置设置如何配置SoftmaxAttentionAttentionSMT:33.3Seq2Se

5、q:34.81RNNSearch(RNNAttn):36.15Transformer:41.8*WMT14 EN/FRTransformerEncoder self attentionDecoder self attentionCross attentionPerformanceAshish Vaswani,Attention is all you needhttps:/ Attention is all you needTransformerNo RNNAttentionPosition embeddingOn the WMT 2014 English-to-French a new sin

6、gle-model SOTA BLEU score of 41.8 after training for 3.5 days on eight GPUshttp:/speech.ee.ntu.edu.tw/tlkagk/courses_DLHLP20.htmlTransformerBLEU 41.83.5 days on eight GPUsPre-trained model familyMore Machine Translation TrendsMulti-device 多设备Multi-screen 多屏幕Real-time 实时Phone/TabletWearableCarLaptop