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| 第17行： |
第17行： |
| | * Ziwei Bai (白子薇) | | * Ziwei Bai (白子薇) |
| | | | |
| − | ==Work Process==
| |
| − | ===Paper Share===
| |
| − | ====2016-06-23====
| |
| − | Learning Better Embeddings for Rare Words Using Distributional Representations [http://aclweb.org/anthology/D15-1033 pdf]
| |
| | | | |
| − | Hierarchical Attention Networks for Document Classification [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf pdf]
| |
| − |
| |
| − | Hierarchical Recurrent Neural Network for Document Modeling [http://www.aclweb.org/anthology/D/D15/D15-1106.pdf pdf]
| |
| − |
| |
| − | Learning Distributed Representations of Sentences from Unlabelled Data [http://arxiv.org/pdf/1602.03483.pdf pdf]
| |
| − |
| |
| − | Speech Synthesis Based on HiddenMarkov Models [http://www.research.ed.ac.uk/portal/files/15269212/Speech_Synthesis_Based_on_Hidden_Markov_Models.pdf pdf]
| |
| − |
| |
| − | ===Research Task===
| |
| − | ====Binary Word Embedding(Aiting)====
| |
| − | [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/97/Binary.pdf binary]
| |
| − |
| |
| − | 2016-06-05: find out that tensorflow does not provide logical derivation method.
| |
| − |
| |
| − | 2016-06-01: complete the first version of binary word embedding model
| |
| − |
| |
| − | 2016-05-28: complete the word2vec model in tensorflow
| |
| − |
| |
| − | 2016-05-25: write my own version of word2vec model
| |
| − |
| |
| − | 2016-05-23:
| |
| − |
| |
| − | 1.get tensorflow's word2vec model from(https://github.com/tensorflow/tensorflow/tree/master/tensorflow/models/embedding)
| |
| − | 2.learn word2vec_basic model
| |
| − | 3.run word2vec.py and word2vec_optimized.py
| |
| − |
| |
| − | 2016-05-22：
| |
| − |
| |
| − | 1.find the tf.logical_xor(x,y) method in tensorflow to compute Hamming distance.
| |
| − | 2.learn tensorflow's word2vec model
| |
| − |
| |
| − | 2016-05-21：
| |
| − |
| |
| − | 1.read Lantian's paper 'Binary Speaker Embedding'
| |
| − | 2.try to find a formula in tensorflow to compute Hamming distance.
| |
| − |
| |
| − | ====Ordered Word Embedding(Aodong)====
| |
| − |
| |
| − | : 2016-07-25, 26, 27, 28, 29 : Share the ACL paper and implement rare word embedding
| |
| − | : 2016-07-18, 19, 20, 21, 22 : Find a scratch paper and ask for the corpora
| |
| − | : 2016-07-13, 14, 15 : Debug the mixed version of Rare Word Embedding
| |
| − | : 2016-07-12 : Complete the mixed initialization version of Rare Word Embedding and start training
| |
| − | : 2016-07-11 :
| |
| − | Improve the predict process of chatting model
| |
| − | Changing some hyperparameters of the chatting model to speed up the training process
| |
| − | : 2016-07-09, 10 : Try to carry out paper's low-freq word experiment, and do some readings both from PRML and Hang Li's paper.
| |
| − | : 2016-07-08 : Do model selections and the model finally set off on the server.
| |
| − | : 2016-07-07 : Complete the chatting model and run on Huilian's server, in order to overcome the GPU memory problem.
| |
| − | : 2016-07-06 : Finally complete translate model in tensorflow!!!! Now I am dealing with the out of memory problem.
| |
| − | : 2016-07-05 :
| |
| − | Code predict process
| |
| − | Although I've got a low cost value, the predict result does not compatible as expected, even the input of predict process from training set
| |
| − | When I tried the Weibo data, program collapsed with an out of memory error.
| |
| − | : 2016-07-04 : Complete Coding training process
| |
| − | : 2016-07-01, 02: The cost function is very bumpy, debug it, while it's quite difficult!
| |
| − | : 2016-06-27, 28, 29 : Coding
| |
| − | : 2016-06-26 : Code tf's GRU and attention model
| |
| − | : 2016-06-25 : Read tf's source code rnn_cell.py and seq2seq.py
| |
| − | : 2016-06-24 :
| |
| − | Code spearman correlation coefficient and experiment
| |
| − | Read Li's paper "Neural Responding Machine for Short-Text Conversation"
| |
| − | : 2016-06-23 :
| |
| − | Share paper "Learning Better Embeddings for Rare Words Using Distributional Representations"
| |
| − | experiment and receive new task
| |
| − | : 2016-06-22 :
| |
| − | Experiment on low-frequency words
| |
| − | Roughly read "Online Learning of Interpretable Word Embeddings"
| |
| − | Roughly read "Learning Better Embeddings for Rare Words Using Distributional Representations"
| |
| − | : 2016-06-21 : Experiment and calculate cosine distance between words
| |
| − | : 2016-06-20 : Something went wrong with my program and fix it, so I have to start it all over again
| |
| − | : 2016-06-04 : Experiment the semantic&syntactic analysis of retrained word vector
| |
| − | : 2016-06-03 : Complete coding retrain process of low-freq word and experiment the semantic&syntactic analysis
| |
| − | : 2016-06-02 : Complete coding predict process of low-freq word and experiment the semantic&syntactic analysis
| |
| − | : 2016-06-01 : Read "Distributed Representations of Words and Phrases and their Compositionality"
| |
| − | : 2016-05-31 :
| |
| − | Read Mikolov's ppt about his word embedding papers
| |
| − | test the randomness of word2vec and there is nothing different in single thread while rerunning the program
| |
| − | Download dataset "microsoft syntactic test set", "wordsim353", and "simlex-999"
| |
| − | : 2016-05-30 : Read "Hierarchical Probabilistic Neural Network Language Model" and "word2vec Explained: Deriving Mikolov's Negative-Sampling Word-Embedding Method"
| |
| − | : 2016-05-27 : Reread word2vec paper and read C-version word2vec.
| |
| − | : 2016-05-24 : Understand word2vec in TensorFlow, and because of some uncompleted functions, I determine to adapt the source of C-versioned word2vec.
| |
| − | : 2016-05-23 :
| |
| − | Basic setup of TensorFlow
| |
| − | Read code of word2vec in TensorFlow
| |
| − | : 2016-05-22 :
| |
| − | Learn about algorithms in word2vec
| |
| − | Read low-freq word papar and learn about 6 strategies
| |
| − |
| |
| − | [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/3/39/How_to_deal_with_low_frequency_words.pdf low_freq]
| |
| − |
| |
| − | [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/2/2c/Lowv.pdf order_rep]
| |
| − |
| |
| − | ====Matrix Factorization(Ziwei)====
| |
| − | [http://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf matrix-factorization]
| |
| − |
| |
| − | 2016-06-23：
| |
| − | prepare for report
| |
| − | 2016-05-28：
| |
| − | learn the code 'matrix-factorization.py','count_word_frequence.py',and 'reduce_rawtext_matrix_factorization.py'
| |
| − | problem:I have no idea how to run the program and where the data.
| |
| − |
| |
| − | 2016-05-23:
| |
| − | read the code 'map_rawtext_matrix_factorization.py'
| |
| − | 2016-05-22：
| |
| − | learn the rest of paper ‘Neural word Embedding as implicit matrix factorization’
| |
| − | 2016-05-21：
| |
| − | learn the ‘abstract’ and ‘introduction’ of paper ‘Neural word Embedding as implicit matrix factorization’
| |
| − |
| |
| − | ===Question answering system===
| |
| − |
| |
| − | ====Chao Xing====
| |
| − | 2016-05-30 ~ 2016-06-04 :
| |
| − | Deliver CDSSM model to huilan.
| |
| − | 2016-05-29 :
| |
| − | Package chatting model in practice.
| |
| − | 2016-05-28 :
| |
| − | Modify bugs...
| |
| − | 2016-05-27 :
| |
| − | Train large scale model, find some problem.
| |
| − | 2016-05-26 :
| |
| − | Modify test program for large scale testing process.
| |
| − | 2016-05-24 :
| |
| − | Build CDSSM model in huilan's machine.
| |
| − | 2016-05-23 :
| |
| − | Find three things to do.
| |
| − | 1. Cost function change to maximize QA+ - QA-.
| |
| − | 2. Different parameters space in Q space and A space.
| |
| − | 3. HRNN separate to two tricky things : use output layer or use hidden layer as decoder's softmax layer's input.
| |
| − | 2016-05-22 :
| |
| − | 1. Investigate different loss functions in chatting model.
| |
| − | 2016-05-21 :
| |
| − | 1. Hand out different research task to intern students.
| |
| − | 2016-05-20 :
| |
| − | 1. Testing denosing rnn generation model.
| |
| − | 2016-05-19 :
| |
| − | 1. Discover for denosing rnn.
| |
| − | 2016-05-18 :
| |
| − | 1. Modify model for crawler data.
| |
| − | 2016-05-17 :
| |
| − | 1. Code & Test HRNN model.
| |
| − | 2016-05-16 :
| |
| − | 1. Work done for CDSSM model.
| |
| − | 2016-05-15 :
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| − | 1. Test CDSSM model package version.
| |
| − | 2016-05-13 :
| |
| − | 1. Coding done CDSSM model package version. Wait to test.
| |
| − | 2016-05-12 :
| |
| − | 1. Begin to package CDSSM model for huilan.
| |
| − | 2016-05-11 :
| |
| − | 1. Prepare for paper sharing.
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| − | 2. Finish CDSSM model in chatting process.
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| − | 3. Start setup model & experiment in dialogue system.
| |
| − | 2016-05-10 :
| |
| − | 1. Finish test CDSSM model in chatting, find original data has some problem.
| |
| − | 2. Read paper:
| |
| − | A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion
| |
| − | A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
| |
| − | Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
| |
| − | Neural Responding Machine for Short-Text Conversation
| |
| − | 2016-05-09 :
| |
| − | 1. Test CDSSM model in chatting model.
| |
| − | 2. Read paper :
| |
| − | Learning from Real Users Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
| |
| − | SimpleDS A Simple Deep Reinforcement Learning Dialogue System
| |
| − | 3. Code RNN by myself in tensorflow.
| |
| − | 2016-05-08 :
| |
| − | Fix some problem in dialogue system team, and continue read some papers in dialogue system.
| |
| − | 2016-05-07 :
| |
| − | Read some papers in dialogue system.
| |
| − | 2016-05-06 :
| |
| − | Try to fix RNN-DSSM model in tensorflow. Failure..
| |
| − | 2016-05-05 :
| |
| − | Coding for RNN-DSSM in tensorflow. Face an error when running rnn-dssm model in cpu : memory keep increasing.
| |
| − | Tensorflow's version in huilan is 0.7.0 and install by pip, this cause using error in creating gpu graph,
| |
| − | one possible solution is build tensorflow from source code.
| |
| − |
| |
| − | ====Aiting Liu====
| |
| − |
| |
| − | 2016-08-08 ~ 2016-08-12 :
| |
| − |
| |
| − | 1.finish the first version of chapter2
| |
| − | 2.read paper "A Sentence Interaction Network for Modeling Dependence between Sentences" [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/0/04/A_Sentence_Interaction_Network_for_Modeling_Dependence_between_Sentences.pdf pdf]]
| |
| − |
| |
| − |
| |
| − | 2016-08-05: write section probabilistic PCA
| |
| − |
| |
| − | 2016-08-04: write section softmax regression
| |
| − |
| |
| − | 2016-08-03: write section logistic regression
| |
| − |
| |
| − | 2016-08-02: write section polynomial fitting and linear regression
| |
| − |
| |
| − | 2016-08-01: learn linear model , determine the content of the chapter2
| |
| − |
| |
| − | 2016-07-28 ~ 2016-07-29: learn lesson linear model
| |
| − |
| |
| − | 2016-07-25 ~ 2016-07-27: read paper Intrinsic Subspace Evaluation of Word Embedding Representations [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/6/68/Intrinsic_Subspace_Evaluation_of_Word_Embedding_Representations.pdf pdf]]
| |
| − |
| |
| − | 2016-07-22 ~ 2016-07-23: run and modify lyrics generation model
| |
| − |
| |
| − | 2016-07-19 ~ 2016-07-21:
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| − |
| |
| − | preprocess the 200,000 lyrics
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| − |
| |
| − | 2016-07-18: get 200,000 songs from http://www.cnlyric.com/ ( singer list from a-z)
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| − |
| |
| − | 2016-07-08: preprocess the lyrics from baidu music
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| − |
| |
| − | 2016-07-07: get 27963 songs from http://www.cnlyric.com/ (singerlist from A/B/C)
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| − |
| |
| − | 2016-07-06: try to get lyrics from http://www.kuwo.cn/ http://www.kugou.com/ http://y.qq.com/#type=index http://music.163.com/
| |
| − |
| |
| − | 2016-07-05: write lyrics spider, and get 56306 songs from http://music.baidu.com/
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| − |
| |
| − | 2016-07-04: learn tensorflow
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| − |
| |
| − | 2016-07-01: submit APSIPA2016 paper
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| − |
| |
| − | 2016-06-30: perfection paper
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| − |
| |
| − | 2016-06-29: complete the ordered word embedding's paper
| |
| − |
| |
| − | 2016-06-26: modify the ordered word embedding's paper
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| − |
| |
| − | 2016-06-25: complete ordered word embedding experiment,get 54 figures
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| − |
| |
| − | 2016-06-23: read Bengio's paper https://arxiv.org/pdf/1605.06069v3.pdf
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| − |
| |
| − | 2016-06-22: read Bengio's paper http://arxiv.org/pdf/1507.04808v3.pdf
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| − |
| |
| − | 2016-06-13:
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| − |
| |
| − | [[文件:Classification.jpg]]
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| − |
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| − | 2016-06-12:
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| − |
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| − | [[文件:Similarity.jpg]]
| |
| − |
| |
| − | 2016-06-05: complete the binary word embedding, find out that tensorflow does not provide logical derivation method.
| |
| − |
| |
| − | 2016-06-04: write the binary word embedding model
| |
| − |
| |
| − | 2016-06-01:
| |
| − |
| |
| − | 1.Record demo video of our Personalized Chatterbot
| |
| − | 2.program the binary word embedding model
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| − |
| |
| − | 2016-05-31: debugging our Personalized Chatterbot
| |
| − |
| |
| − | 2016-05-30: complete our Personalized Chatterbot
| |
| − |
| |
| − | 2016-05-29:
| |
| − |
| |
| − | 1.scan Chao's code and modify it
| |
| − | 2.run the modified program to get the eight hundred thousand sentences's whole matrix
| |
| − |
| |
| − | 2016-05-28:
| |
| − |
| |
| − | 1.complete the word2vec model in tensorflow
| |
| − | 2.complete the first version of binary word embedding model
| |
| − |
| |
| − | 2016-05-25: .write my own version of word2vec model
| |
| − |
| |
| − | 2016-05-23:
| |
| − |
| |
| − | 1.get tensorflow's word2vec model from(https://github.com/tensorflow/tensorflow/tree/master/tensorflow/models/embedding)
| |
| − | 2.learn word2vec_basic model
| |
| − | 3.run word2vec.py and word2vec_optimized.py,we need a Chinese evaluation dataset if we want to use it directly
| |
| − |
| |
| − | 2016-05-22：
| |
| − |
| |
| − | 1.find the tf.logical_xor(x,y) method in tensorflow to compute Hamming distance.
| |
| − | 2.learn tensorflow's word2vec model
| |
| − |
| |
| − | 2016-05-21：
| |
| − |
| |
| − | 1.read Lantian's paper 'Binary Speaker Embedding'
| |
| − | 2.try to find a formula in tensorflow to compute Hamming distance.
| |
| − |
| |
| − | 2016-05-18：
| |
| − |
| |
| − | Fetch American TV subtitles and process them into a specific format(12.6M)
| |
| − | (1.Sex and the City 2.Gossip Girl 3.Desperate Housewives 4.The IT Crowd 5.Empire 6.2 Broke Girls)
| |
| − |
| |
| − | 2016-05-16：Process the data collected from the interview site,interview books and American TV subtitles(38.2M+23.2M)
| |
| − |
| |
| − | 2016-05-11：
| |
| − |
| |
| − | Fetch American TV subtitles
| |
| − | (1.Friends 2.Big Bang Theory 3.The descendant of the Sun 4.Modern Family 5.House M.D. 6.Grey's Anatomy)
| |
| − |
| |
| − | 2016-05-08：Fetch data from 'http://news.ifeng.com/' and 'http://www.xinhuanet.com/'(13.4M)
| |
| − |
| |
| − | 2016-05-07：Fetch data from 'http://fangtan.china.com.cn/' and interview books (10M)
| |
| − |
| |
| − | 2016-05-04：Establish the overall framework of our chat robot,and continue to build database
| |
| − |
| |
| − | ====Ziwei Bai====
| |
| − | 2016-07-29：
| |
| − | download & learn latex
| |
| − | 2016-07-25 ~2016-07-28：
| |
| − | 1、debug the based-RNN TTS（not ideal）
| |
| − | 2、run the based-RNN TTS
| |
| − | 3、write template
| |
| − | 2016-07-21~ 2016-07-23:
| |
| − | build RNN model for TTS
| |
| − | 2016-07-18 ~ 2016-07-19:
| |
| − | 1、run bottleneck model with different parameters
| |
| − | 2、 prepare Bi-weekly report
| |
| − | 3、draw a map to compare different model,
| |
| − | 2016-07-14 ~ 2016-07-15：
| |
| − | build bottleneck model（non linear layer : sigmoid relu tanh）
| |
| − | 2016-07-12 ~ 2016-07-13:
| |
| − | modify the TTS program
| |
| − | 1、separate classify and transfer
| |
| − | 2、separate lf0 and mgc
| |
| − | 2016-07-11：
| |
| − | finish the patent
| |
| − | 2016-07-07 ~ 2016-07-08:
| |
| − | 1、program LSTM with tensorflow （still has some bug）
| |
| − | 2、learn paper 'Fast,Compact,and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers fot Mobile Devices'
| |
| − | 2016-07-06:
| |
| − | finish the second edition of patent
| |
| − | 2016-07-05：
| |
| − | finish the fisrt edition of patent
| |
| − | 2016-07-04：
| |
| − | 1、debug and run the chatting model with softmax
| |
| − | 2、determine model for patent of ‘LSTM-based modern text to the poetry conversion technology’
| |
| − | 2016-07-01：
| |
| − | the model updated yesterday can't converge，try to learn tf.sampled_softmax_loss()
| |
| − | 2016-06-30:
| |
| − | convert our chatting model from Negative sample to softmax and convert the cost from cosine to cross-entropy
| |
| − | tf.softmax()
| |
| − | 2016-06-29：
| |
| − | learn paper 'Neural Responding Machine for Short-Text Conversation'
| |
| − | 2016-06-23：
| |
| − | learn paper ‘Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models’
| |
| − | http://arxiv.org/pdf/1507.04808v3.pdf
| |
| − | 2016-06-22：
| |
| − | 1、construct vector for word cut by jieba
| |
| − | 2、retrain the cdssm model with new word vector(still run)
| |
| − | 2016-06-04：
| |
| − | 1、modify the interface for QA system
| |
| − | 2、pull together the interface and QA system
| |
| − | 2016-06-01：
| |
| − | 1、add data source and Performance Test results in work report
| |
| − | 2、learn pyQt
| |
| − |
| |
| − | 2016-05-30：
| |
| − | complete the work report
| |
| − | 2016-05-29：
| |
| − | write code for inputting a question ,return a answer sets whose question is most similar to the input question
| |
| − | 2016-05-25:
| |
| − | 1、learn DSSM
| |
| − | 2、 complete the first edition of work report
| |
| − | 3、construct basic Q&A（name，age，job and so on）
| |
| − | 2016-05-23：
| |
| − | write code for searching question in 'zhihu.sogou.com' and searching answer in zhihu
| |
| − | 2016-05-21：
| |
| − | learn the second half of paper 'A Neural Conversational Model'
| |
| − | 2016-05-18:
| |
| − | 1、crawl QA pairs from http://www.chinalife.com.cn/publish/zhuzhan/index.html and http://www.pingan.com/
| |
| − | 2、find paper 'A Neural Conversational Model' from google scholar and learn the first half of it.
| |
| − | 2016-05-16:
| |
| − | 1、find datasets in paper 'Neural Responding Machine for Short-Text Conversation'
| |
| − | 2、reconstruct 15 scripts into our expected formula
| |
| − | 2016-05-15:
| |
| − | 1、find 130 scripts
| |
| − | 2、 reconstruct 11 scripts into our expected formula
| |
| − | problem：many files cann't distinguish between dialogue and scenario describes by program.
| |
| − |
| |
| − | 2016-05-11:
| |
| − | 1、read paper“Movie-DiC: a Movie Dialogue Corpus for Research and Development”
| |
| − | 2、reconstruct a new film scripts into our expected formula
| |
| − |
| |
| − | 2016-05-08: convert the pdf we found yesterday into txt，and reconstruct the data into our expected formula
| |
| − |
| |
| − | 2016-05-07: Finding 9 Drama scripts and 20 film scripts
| |
| − |
| |
| − | 2016-05-04：Finding and dealing with the data for QA system
| |
| − |
| |
| − | ====Andi Zhang====
| |
| − | 2016-08-05:
| |
| − | Give a report on my research on sentence similarity
| |
| − |
| |
| − | 2016-08-04:
| |
| − | Give a representation on NNLMs; review papers read earlier this week.
| |
| − |
| |
| − | 2016-08-03:
| |
| − | Read the paper ''Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks''
| |
| − |
| |
| − | 2016-08-02:
| |
| − | Read the paper ''Modeling Interestingness with Deep Neural Networks''
| |
| − |
| |
| − | 2016-08-01:
| |
| − | Read papers about ABCNN for modeling sentence pairs
| |
| − |
| |
| − | 2016-07-25 ~ 2016-07-29:
| |
| − | Read papers about the theories and realization of NNLM, RNNLM & word2vec, prepared for a representation of this topic
| |
| − |
| |
| − | 2016-07-22:
| |
| − | Read papers about CBOW & Skip-gram
| |
| − |
| |
| − | ===Generation Model (Aodong li)===
| |
| − |
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| − | : 2016-05-21 : Complete my biweekly report and take over new tasks -- low-frequency words
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| − | : 2016-05-20 :
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| − | Optimize my code to speed up
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| − | Train the models with GPU
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| − | However, it does not converge :(
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| − | : 2016-05-19 : Code a simple version of keywords-to-sequence model and train the model
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| − | : 2016-05-18 : Debug keywords-to-sequence model and train the model
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| − | : 2016-05-17 : make technical details clear and code keywords-to-sequence model
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| − | : 2016-05-16 : Denoise and segment more lyrics and prepare for keywords to sequence model
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| − | : 2016-05-15 : Train some different models and analyze performance: song to song, paragraph to paragraph, etc.
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| − | : 2016-05-12 : complete sequence to sequence model's prediction process and the whole standard sequence to sequence lstm-based model v0.0
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| − | : 2016-05-11 : complete sequence to sequence model's training process in Theano
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| − | : 2016-05-10 : complete sequence to sequence lstm-based model in Theano
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| − | : 2016-05-09 : try to code sequence to sequence model
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| − | : 2016-05-08 :
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| − | denoise and train word vectors of Lijun Deng's lyrics (110+ pieces)
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| − | decide on using raw sequence to sequence model
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| − | : 2016-05-07 :
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| − | study attention-based model
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| − | learn some details about the poem generation model
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| − | change my focus onto lyrics generation model
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| − | : 2016-05-06 : read the paper about poem generation and learn about LSTM
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| − | : 2016-05-05 : check in and have an overview of generation model
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| − |
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| − | ===jiyuan zhang===
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| − | : 2016-05-01~06 :modify input format and run lstmrbm model (16-beat,32-beat,bar)
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| − | : 2016-05-09~13:
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| − | Modify model parameters and run model ，the result is not ideal yet
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| − | According to teacher Wang's opinion, in the generation stage,replace random generation with the maximum probability generation
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| − |
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| − | : 2016-05-24~27 :check the blog's codes and understand the model and input format details on the blog
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| | | | |
| | ==Past progress== | | ==Past progress== |
