Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion

Abstract

Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive and supportive that inspires and engenders optimism in the minds of people. In contrast to identifying and censoring negative speech patterns, hope speech detection focused on recognising and promoting positive speech patterns online. In this paper, we report an overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English and Spanish languages conducted at the second workshop on Language Technology for Equality, Diversity and Inclusion (LT-EDI-2022), organised as a part of ACL 2022. The participants were provided with annotated training & development datasets and unlabelled test datasets in all five languages. The goal of the shared task is to classify the given sentences into one of the two hope speech classes (Hope speech, Non hope speech). A total of 126 participants registered for the shared task and 14 teams finally submitted their results. The performance of the systems submitted were evaluated in terms of micro-F1 score and weighted-F1 score. The datasets for this challenge are openly available at the competition website.