IDIAP Submission@ LT - EDI - ACL 2022 : Hope Speech Detection for Equality, Diversity and Inclusion (2022)

Abstract: Social media platforms have been provoking masses of people. The individual comments affect a prevalent way of thinking by moving away from preoccupation with discrimination, loneliness, or influence in building confidence, support, and good qualities. This paper aims to identify hope in these social media posts. Hope significantly impacts the well-being of people, as suggested by health professionals. It reflects the belief to achieve an objective, discovers a new path, or become motivated to formulate pathways. In this paper we classify given a social media post, hope speech or not hope speech, using ensembled voting of BERT, ERNIE 2.0 and RoBERTa for English language with 0.54 macro F1-score ( 2^{st} rank). For non-English languages Malayalam, Spanish and Tamil we utilized XLM RoBERTA with 0.50, 0.81, 0.3 macro F1 score ( 1^{st} , 1^{st} , 3^{rd} rank) respectively. For Kannada, we use Multilingual BERT with 0.32 F1 score( 5^{th} )position. We release our code-base here: https://github.com/Muskaan-Singh/Hate-Speech-detection.git .

## introduction and related work hope plays a significant role in well-being, @xcite , recuperation, and restoration of human life by health professionals. hope provides a belief for an individual to discover and utilize their pathways @xcite . it gives the problem-solving ability and coping with various challenges to one objective @xcite @xcite . in this work, we aim to identify this hope through social media comments by individuals as these comments promote confidence, support, good qualities, shifting the vision of thinking from preoccupation with discrimination or loneliness. social media has influenced hate-related crimes or spread hatred. social media platforms such as facebook, youtube, twitter are working tirelessly to detect and bring down such hateful content from their platforms. since hate content must not be confused with freedom of speech and expression, thus it becomes quite challenging to reduce the number of false positives. earlier attempts for hope speech detection, in lt-edi-2021 workshop @xcite involves best-performing model uses a combination of xlm and roberta, xlm-roberta language model @xcite . it also addressed non-english language comments by using tf-idf to filter out the error due to multilingualism and code-mixing after extracting the weighted output of the final layer of the xlm-roberta model. another attempt by @xcite with language identification model to detect non-english hope speech. the classification architecture presented a transformer-based ensembled architecture consisting of a bert pre-trained model and a language identification model. further @xcite , presented a simple classification model which initially created the static bert @xcite embeddings matrix of the data to extract the contextual information of the data and then experimented with various deep neural networks (dnn) to train a binary classifier. motivated from the last year's best performing submission in lt-edi-2021 using the transformers, we ensemble various transformers and utilize the predicted labels with voting. ## shared task description the shared task comprised of hope speech detection for equality, diversity, and inclusion (hopeedi) @xcite . we are provided with social media comments for english, kannada, malayalam, spanish and tamil languages. we participated in all languages. the dataset consists of annotation with hope speech, not hope speech for training development sets, respectively. we have reported the dataset statistics in detail in table3. comment label all lives matter .without that we never have peace so to me forever all lives matter. hope speech only one race the human race hope speech she saves lives with her music. we also did report the hope speech and not speech labels data distribution for all the languages in baseline code with machine learning algorithms (multinomial naive bayes, svm, knn, logistic regression, and decision trees) are also provided to the participants. ## system description in this section, we provide a detailed explanation of our system submission. in this paper, we have proposed a pipeline architecture with data pre-processing section: 3.1, feature extraction in section: 3.2 and proposed ensembled voting model in section:3.3. ## results and analysis we evaluate our model quantitatively and qualitatively for the hopeedi dataset. the classification report for our proposed model with average and best submission among all the teams is reported in table : 8. the proposed model has shown progressive results with 0.55, 0.54, 0.54 f1 for english, tamil, malayalam, kannada, spanish on the leaderboardhttps ://competitions.codalab. org/competitions/36393#learn_the_ details-result with (2 st ,1 st ,1 st ,3 rd ,5 th ) rank respectively. additionally, we also evaluate our prediction results qualitatively in ## conclusion in this paper, we classify given a social media post, hope speech or not hope speech, using ensembled voting of bert, ernie 2.0, and roberta for the english language with 0.54 macro f1-score (2 st rank). for non-english languages malayalam, spanish and tamil we utilized xlm roberta with 0.50, 0.81, 0.3 macro f1 score (1 st , 1 st ,3 rd rank) respectively. for kannada, we use multilingual bert with 0.32 f1 score(5 th )position. we also performed a qualitative analysis. the system performs quite well to recognize the comments for hope speech; in the future, we intend to work on a multi-task learning framework to handle all kinds of hate speech (aggression, misogyny, racism). we also aim to detect multilingual hope speech in the code-mixing scenarios.