  Creating a data-driven model that is trained on a large dataset of
unstructured dialogs is a crucial step in developing Retrieval-based Chatbot
systems. This paper presents a Long Short Term Memory (LSTM) based architecture
that learns unstructured multi-turn dialogs and provides results on the task of
selecting the best response from a collection of given responses. Ubuntu Dialog
Corpus Version 2 was used as the corpus for training. We show that our model
achieves 0.8%, 1.0% and 0.3% higher accuracy for Recall@1, Recall@2 and
Recall@5 respectively than the benchmark model. We also show results on
experiments performed by using several similarity functions, model
hyper-parameters and word embeddings on the proposed architecture
