  In this paper, we describe the systems submitted by our IITP-AINLPML team in
the shared task of SocialNLP 2020, EmotionGIF 2020, on predicting the
category(ies) of a GIF response for a given unlabelled tweet. For the round 1
phase of the task, we propose an attention-based Bi-directional GRU network
trained on both the tweet (text) and their replies (text wherever available)
and the given category(ies) for its GIF response. In the round 2 phase, we
build several deep neural-based classifiers for the task and report the final
predictions through a majority voting based ensemble technique. Our proposed
models attain the best Mean Recall (MR) scores of 52.92% and 53.80% in round 1
and round 2, respectively.
