This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2022 shared tasks. We participated in 2 tasks: a) Task 4: Classification of Tweets self-reporting exact age and b) Task 9: Classification of Reddit posts self-reporting exact age. We evaluated the two( BERT and RoBERTa) transformer based models for both tasks. For Task 4 RoBERTa-Large achieved an F1 score of 0.846 on the test set and BERT-Large achieved an F1 score of 0.865 on the test set for Task 9.

Abusive language content such as hate speech, profanity, and cyberbullying etc., which is common in online platforms is creating lot of problems to the users as well as policy makers. Hence, detection of such abusive language in user-generated online content has become increasingly important over the past few years. Online platforms strive hard to moderate the abusive content to reduce societal harm, comply with laws, and create a more inclusive environment for their users. In spite of various methods to automatically detect abusive languages in online platforms, the problem still persists. To address the automatic detection of abusive languages in online platforms, this paper describes the models submitted by our team - MUCIC to the shared task on “Abusive Comment Detection in Tamil-ACL 2022”. This shared task addresses the abusive comment detection in native Tamil script texts and code-mixed Tamil texts. To address this challenge, two models: i) n-gram-Multilayer Perceptron (n-gram-MLP) model utilizing MLP classifier fed with char-n gram features and ii) 1D Convolutional Long Short-Term Memory (1D Conv-LSTM) model, were submitted. The n-gram-MLP model fared well among these two models with weighted F1-scores of 0.560 and 0.430 for code-mixed Tamil and native Tamil script texts, respectively. This work may be reproduced using the code available in https://github.com/anushamdgowda/abusive-detection.

Spreading positive vibes or hope content on social media may help many people to get motivated in their life. To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team - MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) shared task at Association for Computational Linguistics (ACL) 2022. This shared task consists of texts in five languages, namely: English, Spanish (in Latin scripts), and Tamil, Malayalam, and Kannada (in code-mixed native and Roman scripts) with the aim of classifying the YouTube comment into “Hope”, “Not-Hope” or “Not-Intended” categories. The proposed methodology uses the re-sampling technique to deal with imbalanced data in the corpus and obtained 1st rank for English language with a macro-averaged F1-score of 0.550 and weighted-averaged F1-score of 0.860. The code to reproduce this work is available in GitHub.

Hope is an inherent part of human life and essential for improving the quality of life. Hope increases happiness and reduces stress and feelings of helplessness. Hope speech is the desired outcome for better and can be studied using text from various online sources where people express their desires and outcomes. In this paper, we address a deep-learning approach with a combination of linguistic and psycho-linguistic features for hope-speech detection. We report our best results submitted to LT-EDI-2022 which ranked 2nd and 3rd in English and Spanish respectively.

Social media analytics are widely being explored by researchers for various applications. Prominent among them are identifying and blocking abusive contents especially targeting individuals and communities, for various reasons. The increasing abusive contents and the increasing number of users on social media demands automated tools to detect and filter the abusive contents as it is highly impossible to handle this manually. To address the challenges of detecting abusive contents, this paper describes the approaches proposed by our team MUCIC for Multilingual Gender Biased and Communal Language Identification shared task (ComMA@ICON) at International Conference on Natural Language Processing (ICON) 2021. This shared task dataset consists of code-mixed multi-script texts in Meitei, Bangla, Hindi as well as in Multilingual (a combination of Meitei, Bangla, Hindi, and English). The shared task is modeled as a multi-label Text Classification (TC) task combining word and char n-grams with vectors obtained from Multilingual Sentence Encoder (MSE) to train the Machine Learning (ML) classifiers using Pre-aggregation and Post-aggregation of labels. These approaches obtained the highest performance in the shared task for Meitei, Bangla, and Multilingual texts with instance-F1 scores of 0.350, 0.412, and 0.380 respectively using Pre-aggregation of labels.

This paper describes the participation of the NLP research team of the IPN Computer Research center in the WMT 2020 Similar Language Translation Task. We have submitted systems for the Spanish-Portuguese language pair (in both directions). The three submitted systems are based on the Transformer architecture and used fine tuning for domain Adaptation.

The task of fake news detection is to distinguish legitimate news articles that describe real facts from those which convey deceiving and fictitious information. As the fake news phenomenon is omnipresent across all languages, it is crucial to be able to efficiently solve this problem for languages other than English. A common approach to this task is supervised classification using features of various complexity. Yet supervised machine learning requires substantial amount of annotated data. For English and a small number of other languages, annotated data availability is much higher, whereas for the vast majority of languages, it is almost scarce. We investigate whether machine translation at its present state could be successfully used as an automated technique for annotated corpora creation and augmentation for fake news detection focusing on the English-Urdu language pair. We train a fake news classifier for Urdu on (1) the manually annotated dataset originally in Urdu and (2) the machine-translated version of an existing annotated fake news dataset originally in English. We show that at the present state of machine translation quality for the English-Urdu language pair, the fully automated data augmentation through machine translation did not provide improvement for fake news detection in Urdu.

In recent years, the use of social media has in-creased incredibly. Social media permits Inter-net users a friendly platform to express their views and opinions. Along with these nice and distinct communication chances, it also allows bad things like usage of hate speech. Online automatic hate speech detection in various aspects is a significant scientific problem. This paper presents the Instituto Politécnico Nacional (Mexico) approach for the Semeval 2019 Task-5 [Hateval 2019] (Basile et al., 2019) competition for Multilingual Detection of Hate Speech on Twitter. The goal of this paper is to detect (A) Hate speech against immigrants and women, (B) Aggressive behavior and target classification, both for English and Spanish. In the proposed approach, we used a bag of words model with preprocessing (stem-ming and stop words removal). We submitted two different systems with names: (i) CIC-1 and (ii) CIC-2 for Hateval 2019 shared task. We used TF values in the first system and TF-IDF for the second system. The first system, CIC-1 got 2nd rank in subtask B for both English and Spanish languages with EMR score of 0.568 for English and 0.675 for Spanish. The second system, CIC-2 was ranked 4th in sub-task A and 1st in subtask B for Spanish language with a macro-F1 score of 0.727 and EMR score of 0.705 respectively.

We explore the hypothesis that emotion is one of the dimensions of language that surfaces from the native language into a second language. To check the role of emotions in native language identification (NLI), we model emotion information through polarity and emotion load features, and use document representations using these features to classify the native language of the author. The results indicate that emotion is relevant for NLI, even for high proficiency levels and across topics.

This paper presents the cic_ualg’s system that took part in the Discriminating between Similar Languages (DSL) shared task, held at the VarDial 2017 Workshop. This year’s task aims at identifying 14 languages across 6 language groups using a corpus of excerpts of journalistic texts. Two classification approaches were compared: a single-step (all languages) approach and a two-step (language group and then languages within the group) approach. Features exploited include lexical features (unigrams of words) and character n-grams. Besides traditional (untyped) character n-grams, we introduce typed character n-grams in the DSL task. Experiments were carried out with different feature representation methods (binary and raw term frequency), frequency threshold values, and machine-learning algorithms – Support Vector Machines (SVM) and Multinomial Naive Bayes (MNB). Our best run in the DSL task achieved 91.46% accuracy.

We present the CIC-FBK system, which took part in the Native Language Identification (NLI) Shared Task 2017. Our approach combines features commonly used in previous NLI research, i.e., word n-grams, lemma n-grams, part-of-speech n-grams, and function words, with recently introduced character n-grams from misspelled words, and features that are novel in this task, such as typed character n-grams, and syntactic n-grams of words and of syntactic relation tags. We use log-entropy weighting scheme and perform classification using the Support Vector Machines (SVM) algorithm. Our system achieved 0.8808 macro-averaged F1-score and shared the 1st rank in the NLI Shared Task 2017 scoring.

The paper presents an approach for constructing a weighted bilingual dictionary of inflectional forms using as input data a traditional bilingual dictionary, and not parallel corpora. An algorithm is developed that generates all possible morphological (inflectional) forms and weights them using information on distribution of corresponding grammar sets (grammar information) in large corpora for each language. The algorithm also takes into account the compatibility of grammar sets in a language pair; for example, verb in past tense in language L normally is expected to be translated by verb in past tense in Language L'. We consider that the developed method is universal, i.e. can be applied to any pair of languages. The obtained dictionary is freely available. It can be used in several NLP tasks, for example, statistical machine translation.

We apply word sense disambiguation to the definitions in a Spanish explanatory dictionary. To calculate the scores of word senses basing on the context (which in our case is the dictionary definition), we use a modification of Lesk’s algorithm. The algorithm relies on a comparison between two words. In the original Lesk’s algorithm, the comparison is trivial: two words are either the same lexeme or not; our modification consists in fuzzy (weighted) comparison using a large synonym dictionary and a simple derivational morphology system. Application of disambiguation to dictionary definitions (in contrast to usual texts) allows for some simplifications of the algorithm, e.g., we do not have to care of context window size.