• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• preparing EMNLP

• Pick up new task in news generation and do literature review

• Literature review

• Literature review

• Literature review

• Adjust literature review focus

• Literature review

• Literature review

• Try to reproduce sc-lstm work

• Transfer to new task in machine translation and do literature review

• Literature review

• Literature review

• Literature review and code review

• Code review

• Code review and experiment started, but version discrepancy encountered

• Code review and version discrepancy solved

• Code review and experiment
• details about experiment:

 small data, 
 1st and 2nd translator uses the same training data, 
 2nd translator uses random initialized embedding

• results (BLEU):

 BASELINE: 43.87
 best result of our model: 42.56

• Entry procedures
• Machine Translation paper reading

• experiment setting:

 small data, 
 1st and 2nd translator uses the different training data, counting 22000 and 22017 seperately
 2nd translator uses random initialized embedding

• results (BLEU):

 BASELINE: 36.67 (36.67 is the model at 4750 updates, but we use model at 3000 updates to
                    prevent the case of overfitting, to generate the 2nd translator's training data, for 
                    which the BLEU is 34.96)
 best result of our model: 29.81
 This may suggest that that using either the same training data with 1st translator or different
                   one won't influence 2nd translator's performance, instead, using the same one may
                    be better, at least from results. But I have to give a consideration of a smaller size 
                    of training data compared to yesterday's model.

• code 2nd translator with constant embedding

• Configure environment
• Run tf_translate code
• Read Machine Translation paper

• experiment setting:

 small data, 
 1st and 2nd translator uses the same training data, 
 2nd translator uses constant untrainable embedding imported from 1st translator's decoder

• results (BLEU):

 BASELINE: 43.87
 best result of our model: 43.48
 Experiments show that this kind of series or cascade model will definitely impair the final perfor-
                     mance due to information loss as the information flows through the network from 
                     end to end. Decoder's smaller vocabulary size compared to encoder's demonstrate
                     this (9000+ -> 6000+).
 The intention of this experiment is looking for a map to solve meaning shift using 2nd translator,
                     but result of whether the map is learned or not is obscured by the smaller vocab size 
                     phenomenon.

• literature review on hierarchical machine translation

• Code double decoding model and read multilingual MT paper

• read machine translation paper
• learne lstm model and seq2seq model

• Code doubel decoding model and experiment
• details about experiment:

 small data, 
 2nd translator uses as training data the concat(Chinese, machine translated English), 
 2nd translator uses random initialized embedding

• results (BLEU):

 BASELINE: 43.87
 best result of our model: 43.53

• NEXT: 2nd translator uses trained constant embedding

• understand the difference between lstm model and gru model
• read the implement code of seq2seq model

• read neural machine translation paper
• read tf_translate code

• code and debug double-decoder model
• alter 2017/05/14 model's size and will try after nips

• read neural machine translation paper
• read tf_translate code

• train double-decoder model on small data set but encounter decode bugs

• debug double-decoder model