“NLP Status Report 2017-8-28”版本间的差异

• code refactoring
• wrote a document[[1]]

• read the released information of other toolkits for nmt
• cleaned up the code
• wrote the documents

• write the papers of our baseline system
• read augmented nmt code

• process data and run model;
• test results.

checkpoint-100000 translation model BLEU： 11.11

• source:在秦者名错，与张仪争论,於是惠王使错将伐蜀，遂拔，因而守之。
• target:在秦国的名叫司马错，曾与张仪发生争论，秦惠王采纳了他的意见，于是司马错率军攻蜀国，攻取后，又让他做了蜀地郡守。
• trans：当时秦国的人都很欣赏他的建议，与张仪一起商议，所以吴王派使者率军攻打蜀地，一举攻，接着又下令守城 。
• source:神大用则竭，形大劳则敝，形神离则死 。
• target:精神过度使用就会衰竭，形体过度劳累就会疲惫，神形分离就会死亡。
• trans: 精神过度就可衰竭,身体过度劳累就会疲惫，地形也就会死。
• source:今天子接千岁之统，封泰山，而余不得从行，是命也夫，命也夫！
• target:现天子继承汉朝千年一统的大业，在泰山举行封禅典礼而我不能随行，这是命啊，是命啊！
• trans: 现在天子可以继承帝位的成就爵位，爵位至泰山，而我却未能执行先帝的命运。
• 1.data used Zizhitongjian only(6,000 pairs), we can get BLEU 6 at most.
• 2.data used Zizhitongjian only(12,000 pairs), we can get BLEU 7 at most.
• 3.data used Shiji and Zizhitongjian(43,0000 pairs), we can get BLEU about 9.
• 4.data used Shiji and Zizhitongjian(43,0000 pairs), and split the ancient language text one character by one, we can get BLEU 11.11 at most.
• The main factors now is the data(pairs of sentence/the quality——the modern language text includes context information).

• plan to read source code of seq2seq model and learn tensorflow;
• plan to read a paper named Automatic Long Sentence Segmentation for NMT

• learn model source code

• learn tensorflow and source code