Upamanyu Dass-Vattam


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Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word Embeddings
Upamanyu Dass-Vattam | Spencer Wallace | Rohan Sikand | Zach Witzel | Jillian Tang
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

We propose the use of a contextual embedding based-neural model on strictly textual inputs to detect the presence of patronizing or condescending language (PCL). We finetuned a pre-trained BERT model to detect whether or not a paragraph contained PCL (Subtask 1), and furthermore finetuned another pre-trained BERT model to identify the linguistic techniques used to convey the PCL (Subtask 2). Results show that this approach is viable for binary classification of PCL, but breaks when attempting to identify the PCL techniques. Our system placed 32/79 for subtask 1, and 40/49 for subtask 2.