Amrita@ LT - EDI - EACL 2021: Hope Speech Detection on Multilingual Text (2021)

Abstract: Analysis and deciphering code-mixed data is imperative in academia and industry, in a multilingual country like India, in order to solve problems apropos Natural Language Processing. This paper proposes a bidirectional long short-term memory (BiLSTM) with the attention-based approach, in solving the hope speech detection problem. Using this approach an F1-score of 0.73 (9thrank) in the Malayalam-English data set was achieved from a total of 31 teams who participated in the competition.

## introduction code-mixing may be defined as the phenomenon of mixing of elements of speech or text, from two or more languages, into a single utterance. code-mixing is widely observed in social socialmedia platforms @xcite . code mixing imposes diverse challenges in natural language processing (nlp) tasks like sentiment analysis (sa), named entity recognition (ner), and parts-of-speech (pos) tagging @xcite . nowadays, as more people lean on online communication platforms, it is incumbent on bloggers to stop the spread of negativity through online posts @xcite @xcite . studies have been carried out to surveil and cease the consumption of abusive comments on social-media platforms like you-tube and twitter @xcite @xcite . besides curbing dissemination of negativity, it is imperative for users online to endorse and promote positivity and hope @xcite . as studies have shown that hope is an important factor in prevention of self-immolation and self-harm @xcite . hope is generally attributed as assurance, encouragement, support, or motivation instilled in individuals by their peers or surroundings chakravarthi (2020a); puranik et al. (2021); ghanghor et al. (2021b) however, there is not much study done in understanding hope speech in the domain of code mixed communications. hope speech detection for equality, diversity, and inclusion-eacl 2021 is first such task foot_0 to detect hope speech in low resourced code-mixed languages like malayalam-english, tamil-english, kannada-english. ## literature survey since hope speech detection for equality, diversity, and inclusion-eacl 2021 is the first task in hope speech detection, there is not much focus in this area. most of the studies have been done in the nlp aspects of ner, sa and pos tagging on code mixed data. in @xcite the authors experimented with various word embedding techniques using 6 different classifiers and showed that term frequency and inverse document frequency (tf-idf) word embeddings yielded better results for ner task on english-hindi code-mixed data. in @xcite the authors discusses on the literature survey of code-mixed problems and datasets. in @xcite authors created a corpus of 12000 hindi-english code mixed texts from various online communication platforms and annotated them as happy, sad and anger based on the emotions. the authors achieved an accuracy of 83.21% using cnn-bilstm architecture. in @xcite the authors have used support vector machine (svm)-radial basis funcrion (rbf) classifier on features extracted using fasttext for hate speech detection in hindi-english code mixed corpus of 10000 texts which are equally divided into hate and non-hate classes. the authors obtained an accuracy of 0.75% using svm-rbf algorithm. in liu et al. (2020) used an adversarial training with a pre-trained multilingual model xlm-r conneau et al. (2020) on code-mixed hindi-english data 2 and achieved an f1-score of 0.75 (1 st rank) for sa. ## experiments and results we implemented all our models in tensorflow foot_2 . ## conclusion we evaluated the performance of our models on the test data using sklearn f1-score foot_3 , we found that 1-d cnn on up-sampled data with both word2vec and fasttext embeddings performed better than the bi-lstm model which we submitted for the task. however, bi-lstm performed relatively better on the development data set (see