“NLP Status Report 2017-4-5”版本间的差异

2017年4月5日 (三) 02:15的版本

Date	People	Last Week	This Week
2017/4/5	Yang Feng	Got the sampled 100w good data and ran Moses (BLEU: 30.6) Reimplemented the idea of ACL (added some optimization to the previous code) and check the performance in the following gradual steps: 1. use s\_i-1 as memory query; 2. use s\_i-1+c\_i as memory query; 3. use y as the memory states for attention; 4. use y + smt_attentions * h as memory states for attention. ran experiments for the above steps but the loss was inf. I am looking for reasons.	do experiments and write the paper
	Jiyuan Zhang	I did keyword expansion on the qx's model fixed some bugs read two papers	improve the effect of the qx's model
	Andi Zhang	revise the original oov model so that it can automatically detect oov words and translate them deal with the situation that source word is oov but target word is not oov first it didn't predict right	make the model work as what we wanted deal with the situation that source word is oov and target word is also oov, then other situations
	Shiyue Zhang	got a reasonable baseline on big zhen data	implement mem model on this baseline, and test on big data
	Peilun Xiao

@@ 第5行： / 第5行： @@
 |Yang Feng ||
 * Got the sampled 100w good data and ran Moses (BLEU: 30.6)
-* Reimplemented the idea of ACL (added some optimization to the previous code) and check the performance in the following gradual steps: 1. use s\_i-1 as memory query; 2. use s\_i-1+c\_i
+* Reimplemented the idea of ACL (added some optimization to the previous code) and check the performance in the following gradual steps: 1. use s\_i-1 as memory query; 2. use s\_i-1+c\_i as memory query; 3. use y as the memory states for attention; 4. use y + smt_attentions * h as memory states for attention.
- as memory query; 3. use y as the memory states for attention; 4. use y + smt_attentions * h as memory states for attention.
 * ran experiments for the above steps but the loss was inf. I am looking for reasons.
 ||