Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes. Different languages account for different cognitive triggers, however there seems to be some uniform indicatorsacross languages. In this paper, we describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multi-lingual dataset. 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. We train an end-to-end model to extract meaningful information from different languages and test our model on two separate datasets. We compare different transformer models andshow ablation studies affecting model performance. Our final submission ranked 4th place for SubTask-1 and 1st place for SubTask-2 forthe shared task.

In recent years, there has been an upsurge in a new form of entertainment medium called memes. These memes although seemingly innocuous have transcended the boundary of online harassment against women and created an unwanted bias against them. To help alleviate this problem, we propose an early fusion model for the prediction and identification of misogynistic memes and their type in this paper for which we participated in SemEval-2022 Task 5. The model receives as input meme image with its text transcription with a target vector. Given that a key challenge with this task is the combination of different modalities to predict misogyny, our model relies on pre-trained contextual representations from different state-of-the-art transformer-based language models and pre-trained image models to get an effective image representation. Our model achieved competitive results on both SubTask-A and SubTask-B with the other competingteams and significantly outperforms the baselines.

The paper describes the work that the team submitted to FinCausal 2020 Shared Task. 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. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.