chatbot-trainer

A chrome extension and related code for training the chatbot LightBlue.

A demo video: https://youtu.be/flt2GLLF8so

Workflow

The workflow of the whole set of code is:

1. Extract vocabulary words from the html page and save to file voc.py
2. Vocabulary analysis by word2vec and then plot the relationship between words
3. Do data preprocessing on raw training data
4. Manually revise the processed training data
5. Check vocabulary for training data
6. Train the chatbot by a chrome extension

Details will be explained below.

Vocabulary Extraction

• Input html file: /chatbot_metadata/voc.html
• Expected Output txt file: voc.txt
• Not common words are saved at: voc_special.txt

Run:

python extract_voc.py

Vocabulary analysis

Run:

sh setup.txt
cd word2vec
python word2vec.py

This TensorFlow program will download training data from http://mattmahoney.net/dc/text8 and train the word2vec network to obtain word embedding vectors for 50000 common words. Then you can get a plot of words in voc.txt to see their relationships. A sample plot can be found at /word2vec/tsne_1000.png.

Datasets

In my case, my training data is from eslfast.com.

• Put raw training data at /chrome_ext/data/topics_original/$INDEX$
• Expected formatted data will be saved at /chrome_ext/data/topics/$INDEX$

Change the startind at data_processing/eslfast/main.py to $INDEX$, then run:

cd data_processing/eslfast
python main.py

Vocabulary Checking

• Input data is the modified conversations at /chrome_ext/data/topics/$INDEX$

Run

python check_voc.py

to see the words that not in voc.txt.

Chrome Extension

• Load the unpacked extension in folder /chrome_ext to your Chrome follows official guide
• Choose training file
• Click start

What will the extension do during training?

1. Input the $QUESTION$
2. Modify the response to $ANSWER$ and change
3. Like the revised response
4. Train next one

The extension will start new session when meet <sss> in training document, Q&A should be formatted as $QUESTION$>>>$ANSWER$. Sample training paragraph:

welcome, smallblue, come on in!>>>hi, derek! what a nice home!
we enjoy it too!>>>how long have you live here?
about four year now.>>>well, it is very beautiful.
smallblue, have a seat and I will get us something to drink.>>>good! I am really hot. you know it really is hot outside!
I have different drink.>>>thank you!
<sss>
smallblue, welcome to my home!>>>it is so nice to see you. what a wonderful home!
we really like stay in this neighborhood.>>>how long have you have this house?
we just move here last year.>>>it is a beautiful home.
I get some drink for us in the kitchen.>>>that would be wonderful. it is really hot out.
I can offer you drink.>>>thank you!
<sss>

Author

Derek Mingyu MA, derek.ma, [email protected]

Acknowledgements

Most of the word2vec implementation in TensorFlow is borrowed from https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/examples/tutorials/word2vec/word2vec_basic.py.

The paper of word2vec can be found at: https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf