EmotionGIF-IITP-AINLPML: Ensemble-based Automated Deep Neural System for predicting category(ies) of a GIF response
This work addresses the problem of automatically categorizing GIF responses for tweets, which is relevant for social media analysis and automated content moderation.
The paper describes the IITP-AINLPML team's systems for predicting GIF response categories for tweets, achieving Mean Recall scores of 52.92% in round 1 and 53.80% in round 2 of the EmotionGIF 2020 shared task.
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.