  There is a key demand to automatically generate code for small tasks for
developers. Websites such as StackOverflow provide a simplistic way by offering
solutions in small snippets which provide a complete answer to whatever task
question the developer wants to code. Natural Language Processing and
particularly Question-Answering Systems are very helpful in resolving and
working on these tasks. In this paper, we develop a two-fold deep learning
model: Seq2Seq and a binary classifier that takes in the intent (which is in
natural language) and code snippets in Python. We train both the intent and the
code utterances in the Seq2Seq model, where we decided to compare the effect of
the hidden layer embedding from the encoder for representing the intent and
similarly, using the decoder's hidden layer embeddings for the code sequence.
Then we combine both these embeddings and then train a simple binary neural
network classifier model for predicting if the intent is correctly answered by
the predicted code sequence from the seq2seq model. We find that the hidden
state layer's embeddings perform slightly better than regular standard
embeddings from a constructed vocabulary. We experimented with our tests on the
CoNaLa dataset in addition to the StaQC database consisting of simple task-code
snippet-based pairs. We empirically establish that using additional pre-trained
embeddings for code snippets in Python is less context-based in comparison to
using hidden state context vectors from seq2seq models.
