We propose a multimodal multitask learning approach for ER from individual utterances in isolation. Experiments on the IEMOCAP benchmark show that our proposed model performs better than our re-implementation of state-of-the-art and achieves better performance than all other unimodal and multimodal approaches in literature.

We propose a novel architecture boosted by a modified LSTM cell, which we call DiscLSTM, that better captures the interaction between conversational and sequential context. DiscLSTM brings together the best of both worlds and provides a more intuitive and efficient way to model the information flow between individual utterances by better capturing long-distance conversational background through discourse relations and sequential context through recurrence.

We propose an early fusion model for prediction and identification of misogynistic memes and its type in this paper for which we participated in SemEval-2022 Task 5

Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation for 2 main eye-tracking features.

This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences.