“NLP Status Report 2016-11-21”版本间的差异

 rnng+mn

• ran experiments of rnng+mn [report]
• used top-k for memory, under training

 sequence-to-sequence + mn

• wrote the proposal
• discussed the details of Andy

  Intern interview
  Huilan's work

• prepare new data set for paraphrase, wiped out repetition & most of the noises
• run NMT on fr-en data set and new paraphrase set
• read through source code to find ways to modify it
• helped Guli with running NMT on our server

• decide to drop theano or not
• start to work on codes

• run rnng on MKL successfully, which can double or triple the speed.
• rerun the original model and get the final result 92.32
• rerun the wrong memory models, still running
• implement the dynamic memory model and get the result 92.54 which is 0.22 better than baseline
• try another structure of memory

• try more different models and summary the results
• publish the technical reports