ur work is very perfect!
But the raw paper use the previous embedding and cell_state as input to calculate current cell_state as context left.your code use normal RNN(current embedding and previous cell_state) to calculate the cell_state.
I try write code as raw paper,but i find it is very slow.
what is your opinion?

Thanks for your code. But I have a problem. In the raw paper, theta represent all paramaters ,including "E; b(2); b(4); cl(w1); cr(wn); W (2);W (4); W (l); W (r); W (sl); W (sr)", these are all trained while training. But in your code, there are no "W (l); W (r); W (sl); W (sr)" and "cl(w1)" always setted to be zero vector because of the use of biRNN, is that a problem?Thanks!

i have tried to run your code but embedding part is not running. Its giving me various errors such as "vocab_size is not defined", "word_embedding_size is not defined" and "self is not defined". Please tell me how to get rid of these problems.

text_vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(FLAGS.max_sentence_length)

Seems like when tensorflow updated to version 2 they left out tf.contrib especially the VocabularyProcessor.
Is there an alternative for this line to make the rest of your code still work? Thanks

Thanks for your code ! I wanna ask what is your accuracy on the training set and dev set during the training process ? I am a little bit confused because my best result is 0.67 for the training set, 0.55 for the dev set. I do not use pre-training word2vec, num_epochs was set to 50, and other experiment configurations are just default

Calssification 을 Classification 로 고치셔야 할 것 같습니다.

Hello!
your code is really perfect,but i wonder whether your project support Multi-Classification by modify the array value in func load_data_and_labels()?
thx!

hello,

As we all known, the length of text is not fixed. and why you write it like the picture?

Thanks for your code,however,I have a problem when running "eval.py".

File "F:\Program Files (x86)\python3.6\00\lib\site-packages\tensorflow\contrib\learn\python\learn\preprocessing\text.py", line 246, in restore
return pickle.loads(f.read())
File "F:\Program Files (x86)\python3.6\00\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 125, in read self._preread_check()
File "F:\Program Files (x86)\python3.6\00\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 85, in _preread_check
compat.as_bytes(self.__name), 1024 * 512, status)
File "F:\Program Files (x86)\python3.6\00\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 526, in exit
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.NotFoundError: NewRandomAccessFile failed to Create/Open: ..\text_vocab : ϵͳ\udcd5Ҳ\udcbb\udcb5\udcbdָ\udcb6\udca8\udcb5\udcc4\udcceļ\udcfe\udca1\udca3
; No such file or directory

Please tell me how can I get rid of this bug,thanks a lot!

hello,

As we all known, the length of text is not fixed. and why you write it like the picture?

Thanks for sharing code. It seems that your implement of context representation vectors (via Bi-RNN) differ from the original paper's (Equation 1&2). Have you compare the performance between them and what's your intuition? :)

Thanks for sharing code!
I have read https://zhuanlan.zhihu.com/p/42201550, in which there is avctivition function tanh after w_2 * x + b_2。
but the code dont have this
Is there something I missed?