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| "paper_id": "2021", | |
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| "date_generated": "2023-01-19T12:12:07.792325Z" | |
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| "title": "Hopeful NLP@LT-EDI-EACL2021: Finding Hope in YouTube Comment Section", | |
| "authors": [ | |
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| "first": "Vasudev", | |
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| "last": "Awatramani", | |
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| "institution": "Agrasen Institute of Technology New Delhi", | |
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| "abstract": "The proliferation of Hate Speech and misinformation in social media is fast becoming a menace to society. In compliment, the dissemination of hate-diffusing, promising and anti-oppressive messages become a unique alternative. Unfortunately, due to its complex nature as well as the relatively limited manifestation in comparison to hostile and neutral content, the identification of Hope Speech becomes a challenge. This work revolves around the detection of Hope Speech in Youtube comments, for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion (Chakravarthi and Muralidaran, 2021). We achieve an f-score of 0.93, ranking 1 st on the leaderboard for English comments.", | |
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| "text": "The proliferation of Hate Speech and misinformation in social media is fast becoming a menace to society. In compliment, the dissemination of hate-diffusing, promising and anti-oppressive messages become a unique alternative. Unfortunately, due to its complex nature as well as the relatively limited manifestation in comparison to hostile and neutral content, the identification of Hope Speech becomes a challenge. This work revolves around the detection of Hope Speech in Youtube comments, for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion (Chakravarthi and Muralidaran, 2021). We achieve an f-score of 0.93, ranking 1 st on the leaderboard for English comments.", | |
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| "text": "With the rampant adoption of social media, problems like hostile speech detection have caught extensive attention in Natural Language Processing (NLP) research Suryawanshi and Chakravarthi, 2021) . However, there has been little work on the identification of text that promotes positivity and social well-being. As social media becomes predominant in the daily lives of people, it is crucial, not only to protect users from hateful and discriminative content but also encourage communication that triggers optimism and hope. Such expression in a narrow sense may be referred to as Hope Speech and its identification in the digital space as Hope Speech Detection (Puranik et al., 2021; Ghanghor et al., 2021) . However, such a task is further challenging as the definition is highly subjective and evolving. Neutral or Positive content with no indication of hostility is not necessarily a sufficient determinant. Advocacy of ideas that promote social well-being, ethics, equality, inclusion, tolerance, diversity, a fair representation of minorities, or either the appreciation or motivation for an individual or a group, are a few indicators of Hope Speech. Moreover, criticism of oppressive or malicious elements of society may also fall under such a category. Furthermore, one does not need to express such beliefs with the present but may incorporate past or future developments.", | |
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| "text": "Suryawanshi and Chakravarthi, 2021)", | |
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| "text": "(Puranik et al., 2021;", | |
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| "section": "Introduction", | |
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| "text": "Hope Speech Detection is a nascent research task by (Palakodety et al., 2020) . The authors proposed automatic identification of positive web content that may diffuse hostility on social media platforms due to political tensions around the 2019 Pulwama Terror Attack 1 . The authors mined a multilingual (Hindi and English) corpus by scrapping comments from Youtube videos related to the crisis. The authors developed a comprehensive system that took statistical NLP features: n-grams, along with temporal sentiment scores. The system also employed language identification using polyglot FastText (Bojanowski et al., 2017) , to achieve an F1-score of 78.51 and 95.48 AUC. However, the work differs from HopeEDI (Chakravarthi, 2020) as it focuses on alleviation of tension and violence and ignores other aspects of hope.", | |
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| "text": "(Palakodety et al., 2020)", | |
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| "section": "Related Work", | |
| "sec_num": "2" | |
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| "text": "The study focuses on HopeEDI dataset as part of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion. We compare our experimental outcomes with the results with (Chakravarthi, 2020) as the baseline. Chakravarthi (2020) employed TFIdf: token frequency-inverse document frequency along with classifiers such as Multinomial Naive Bayes, K-nearest neighbours, Support Vector Machine, Decision Tree and Logistic Regression. Decision tree delivered the highest F-Score for English and Malayalam while Tamil performed well with Logistic Regression. A more thorough comparison occurs in Section 4.", | |
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| "text": "The HopeEDI dataset consists of 3 languages: English, Tamil and Malayalam. The task can be addressed as a sequence classification problem with 3 class: Hope Speech, Not Hope Speech and Not belonging to the given language. Weighted F1 score is employed as an evaluation metric over the 3 languages separately. Moreover, Tamil and Malayalam consist of text samples in romanized and native scripts. This section describes the experiments conducted for the task and states their outcomes over the validation set.", | |
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| "section": "Experiments", | |
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| "text": "Customarily, models involving NLP Tasks were trained after random initialization of the network parameters. In Transfer Learning, a neural network is fine-tuned on a particular task after being pretrained on a general task enabling a given neural network to converge faster and lesser amount of data. Originally, transfer learning has been mainly linked with the fine-tuning of deep learning models trained on the ImageNet dataset (Deng et al., 2009) . Recently, the field of NLP has witnessed the emergence of various transfer learning techniques and architectures which considerably improved upon the state-of-the-art on a wide array of NLP tasks. Transfer learning can be employed for applications where there is a lack of availability of sufficient training data. The target dataset should ideally be related to the priorly trained dataset for effective learning. This nature of training is generally attributed as Semi-Supervised training where the network is first trained as a language model on a comprehensive dataset followed by supervised training on a labelled training dataset.", | |
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| "text": "We evaluate such models for the task of hostilitydiffusing speech detection, trained over a batch size of 128 over 4 epochs. For English, we experiment with BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) .", | |
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| "text": "(Liu et al., 2019)", | |
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| "section": "Transfer Learning", | |
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| "text": "F1-Score BERT-base-cased 0.9230 BERT-large-cased 0.9253 RoBERTa-base 0.9313 RoBERTa-large 0.9261 Adversarial perturbations attempt to fool models by feeding deceptive input. In general, when a data sample is perturbed, they appear to maintain the same fidelity for humans but manage to get the confuse model prediction. The model mispredicts the target for the perturbed sample as opposed to predicting correctly in the original scenario.", | |
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| "section": "Model", | |
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| "text": "Paraphrasing (Lei et al., 2019) is one such attack that preserves both semantic meaning and syntactically validity as well as transforms text into suitable replacements. However, we apply an earlier variation of sentence-level paraphrasing (Mallinson et al., 2017) as a means for language-targetted data augmentation. In this approach, we consider Dutch as the translating language. Both English and Dutch fall under the West Germanic hierarchy of Proto-Germanic languages, with English being Anglo-Frisian and Dutch being Netherlandic-German. Therefore, by choosing a similar language, the generated paraphrases do not lose much semantic meaning but add variability for effective data augmentation. It may noted, this approach cannot be extended to other languages in the task such as Malayalam and Tamil, because translation method (googletrans) employed cannot handle romanized and transliterated text effectively.", | |
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| "section": "Model", | |
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| "text": "F1-Score BERT + Paraphrasing Aug. 0.90 RoBERTa + Paraphrasing Aug. 0.90 ", | |
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| "section": "Approach", | |
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| "text": "Following are described results of the task evaluated over the test set. Our system ranked 1 st overall with F1-Score of 0.93 for English, and 4 th for Malayalam with 0.78 F-Score.", | |
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| "section": "Results", | |
| "sec_num": "4" | |
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| { | |
| "text": "F1-Score Baseline (Chakravarthi, 2020) 0.90 roBERTa-base 0.90 BERT-large-cased 0.88 ", | |
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| "text": "In this work, we present a simple means of Hope Speech Identification using Pre-trained transformers and Paraphrasing Generation for Data Augmentation. Our future work shall concentrate on interpretability, specifically answering questions like what makes a text an instance of Hope Speech. Moreover, we will attempt to couple more modalities with text, such as audio recording and images or even video clips that collectively promote hope speech.", | |
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