The task ValueEval aims at assigning a sub- set of possible human value categories under- lying a given argument. Values behind argu- ments are often determinants to evaluate the relevance and importance of decisions in eth- ical sense, thereby making them essential for argument mining. The work presented here proposes two systems for the same. Both sys- tems use RoBERTa to encode sentences in each document. System1 makes use of features ob- tained from training models for two auxiliary tasks, whereas System2 combines RoBERTa with topic modeling to get sentence represen- tation. These features are used by a classifi- cation head to generate predictions. System1 secured the rank 22 in the official task rank- ing, achieving the macro F1-score 0.46 on the main dataset. System2 was not a part of official evaluation. Subsequent experiments achieved highest (among the proposed systems) macro F1-scores of 0.48 (System2), 0.31 (ablation on System1) and 0.33 (ablation on System1) on the main dataset, the Nahj al-Balagha dataset, and the New York Times dataset.

Natural Language Processing techniques can be leveraged to process legal proceedings for various downstream applications, such as sum- marization of a given judgement, prediction of the judgement for a given legal case, prece- dent search, among others. These applications will benefit from legal judgement documents already segmented into topically coherent units. The current task, namely, Rhetorical Role Pre- diction, aims at categorising each sentence in the sequence of sentences in a judgement document into different labels. The system proposed in this work combines topic mod- eling and RoBERTa to encode sentences in each document. A BiLSTM layer has been utilised to get contextualised sentence repre- sentations. The Rhetorical Role predictions for each sentence in each document are gen- erated by a final CRF layer of the proposed neuro-computing system. This system secured the rank 12 in the official task ranking, achiev- ing the micro-F1 score 0.7980. The code for the proposed systems has been made available at https://github.com/KushagriT/SemEval23_ LegalEval_TeamLRL_NC

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.