Patronizing and condescending language (PCL) can find its way into many mediums of public discourse. Presence of PCL in text can produce negative effects in the society. The challenge presented by the task emerges from the subtleties of PCL and various data dependent constraints. Hence, developing techniques to detect PCL in text, before it is propagated is vital. The aim of this paper is twofold, a) to present systems that can be used to classify a text as containing PCL or not, and b) to present systems that assign the different categories of PCL present in text. The proposed systems are primarily rooted in transformer-based pre-trained language models. Among the models submitted for Subtask 1, the best F1-Score of 0.5436 was achieved by a deep learning based ensemble model. This system secured the rank 29 in the official task ranking. For Subtask 2, the best macro-average F1-Score of 0.339 was achieved by an ensemble model combining transformer-based neural architecture with gradient boosting label-balanced classifiers. This system secured the rank 21 in the official task ranking. Among subsequently carried out experiments a variation in architecture of a system for Subtask 2 achieved a macro-average F1-Score of 0.3527.

Reading and comprehension are quintessentially cognitive tasks. Eye movement acts as a surrogate to understand which part of a sentence is critical to the process of comprehension. The aim of the shared task is to predict five eye-tracking features for a given word of the input sentence. We experimented with several models based on LGBM (Light Gradient Boosting Machine) Regression, ANN (Artificial Neural Network), and CNN (Convolutional Neural Network), using BERT embeddings and some combination of linguistic features. Our submission using CNN achieved an average MAE of 4.0639 and ranked 7th in the shared task. The average MAE was further lowered to 3.994 in post-task evaluation.