  With the increased use of social media platforms by people across the world,
many new interesting NLP problems have come into existence. One such being the
detection of sarcasm in the social media texts. We present a corpus of tweets
for training custom word embeddings and a Hinglish dataset labelled for sarcasm
detection. We propose a deep learning based approach to address the issue of
sarcasm detection in Hindi-English code mixed tweets using bilingual word
embeddings derived from FastText and Word2Vec approaches. We experimented with
various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with
and without attention). We were able to outperform all state-of-the-art
performances with our deep learning models, with attention based Bi-directional
LSTMs giving the best performance exhibiting an accuracy of 78.49%.
