Unique way of AI-Chatbots (Dynamic memory networks#nlp)
Deep learning has made it possible to make AI Chatbots using word2vec, seq2seq, lstm,gru……
Dynamic Memory networks-(DMNs) are state of art in Q&A systems. All nlp tasks r Q&A type. The way we use to build chatbots is to provide a series of input sentences and ask a question based on that sentences and it’s gonna output the answer.
INPUT: there is a dog-dog is black
Query: which color is dog ?
OUTPUT : black (#Literally,we can ask it anything like whats translation or whats sentiment)
There was also a kaggle competition (https://www.kaggle.com/c/the-allen-ai-science-challenge).We can use the fb research dataset called-babi (https://research.fb.com/downloads/babi/) which has
many tasks consisting of inputs,queries and answers.
Input vectors are SEMANTIC memory.Then we can split in train/test sets and vectorize data into 3 parts viz. inputs,queries,answers.
Then we create a EPISODIC memory (output vectors).Further we compile our model and fit it.
Dynamic memory network has 2 modules: Semantic & Episodic.These were invented considering idea behind our brain’s Hippocampus working.
Episodic memory has GRU’s(graded rnn) which are better than lstm.A good post to understand gru-(https://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be).
Here we need more episodes because our model should know which part of sentence it should pay attention to.
We can test our model ourselves,but a genious called Ethan Caballero has made a DMN+web app for fb’s babi tasks where we can choose our input task and let Chatbot predict answer of question
link to webapp: https://ethancaballero.pythonanywhere.com/
link to code:https://github.com/keras-team/keras/blob/master/examples/babi_memnn.py(#credit for code goes to francois chollet,I have merely created a wrapper to understand)
