pwc-archive/papers-with-abstracts huggingface dataset:
Findings of the Shared Task on Offensive Language Identification in Tamil, Malayalam, and Kannada
Authors: Bharathi Raja Chakravarthi, Ruba Priyadharshini, Navya Jose, Anand Kumar M, Thomas Mandl, Prasanna Kumar Kumaresan, Rahul Ponnusamy, Hariharan R L, John P. McCrae, Elizabeth Sherly
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Benchmarking, Language Identification

Detecting offensive language in social media in local languages is critical for moderating user-generated content. Thus, the field of offensive language identification in under-resourced Tamil, Malayalam and Kannada languages are essential. As the user-generated content is more code-mixed and not well studied for under-resourced languages, it is imperative to create resources and conduct benchmarking studies to encourage research in under-resourced Dravidian languages. We created a shared task on offensive language detection in Dravidian languages. We summarize here the dataset for this challenge which are openly available at https://competitions.codalab.org/competitions/27654, and present an overview of the methods and the results of the competing systems.
GX@DravidianLangTech-EACL2021: Multilingual Neural Machine Translation and Back-translation
Authors: Wanying Xie
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Decoder, Machine Translation, Translation

In this paper, we describe the GX system in the EACL2021 shared task on machine translation in Dravidian languages. Given the low amount of parallel training data, We adopt two methods to improve the overall performance: (1) multilingual translation, we use a shared encoder-decoder multilingual translation model handling multiple languages simultaneously to facilitate the translation performance of these languages; (2) back-translation, we collected other open-source parallel and monolingual data and apply back-translation to benefit from the monolingual data. The experimental results show that we can achieve satisfactory translation results in these Dravidian languages and rank first in English-Telugu and Tamil-Telugu translation.
OFFLangOne@DravidianLangTech-EACL2021: Transformers with the Class Balanced Loss for Offensive Language Identification in Dravidian Code-Mixed text.
Authors: Suman Dowlagar, Radhika Mamidi
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification, Transliteration

The intensity of online abuse has increased in recent years. Automated tools are being developed to prevent the use of hate speech and offensive content. Most of the technologies use natural language and machine learning tools to identify offensive text. In a multilingual society, where code-mixing is a norm, the hate content would be delivered in a code-mixed form in social media, which makes the offensive content identification, further challenging. In this work, we participated in the EACL task to detect offensive content in the code-mixed social media scenario. The methodology uses a transformer model with transliteration and class balancing loss for offensive content identification. In this task, our model has been ranked 2nd in Malayalam-English and 4th in Tamil-English code-mixed languages.
Simon @ DravidianLangTech-EACL2021: Detecting Offensive Content in Kannada Language
Authors: Qinyu Que
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

This article introduces the system for the shared task of Offensive Language Identification in Dravidian Languages-EACL 2021. The world’s information technology develops at a high speed. People are used to expressing their views and opinions on social media. This leads to a lot of offensive language on social media. As people become more dependent on social media, the detection of offensive language becomes more and more necessary. This shared task is in three languages: Tamil, Malayalam, and Kannada. Our team takes part in the Kannada language task. To accomplish the task, we use the XLM-Roberta model for pre-training. But the capabilities of the XLM-Roberta model do not satisfy us in terms of statement information collection. So we made some tweaks to the output of this model. In this paper, we describe the models and experiments for accomplishing the task of the Kannada language.
Codewithzichao@DravidianLangTech-EACL2021: Exploring Multilingual Transformers for Offensive Language Identification on Code Mixing Text
Authors: Zichao Li
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

This paper describes our solution submitted to shared task on Offensive Language Identification in Dravidian Languages. We participated in all three of offensive language identification. In order to address the task, we explored multilingual models based on XLM-RoBERTa and multilingual BERT trained on mixed data of three code-mixed languages. Besides, we solved the class-imbalance problem existed in training data by class combination, class weights and focal loss. Our model achieved weighted average F1 scores of 0.75 (ranked 4th), 0.94 (ranked 4th) and 0.72 (ranked 3rd) in Tamil-English task, Malayalam-English task and Kannada-English task, respectively.
JudithJeyafreedaAndrew@DravidianLangTech-EACL2021:Offensive language detection for Dravidian Code-mixed YouTube comments
Authors: Judith Jeyafreeda Andrew
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4

Title: JudithJeyafreedaAndrew@DravidianLangTech-EACL2021:Offensive language detection for Dravidian Code-mixed YouTube comments Author: Judith Jeyafreeda Andrew Messaging online has become one of the major ways of communication. At this level, there are cases of online/digital bullying. These include rants, taunts, and offensive phrases. Thus the identification of offensive language on the internet is a very essential task. In this paper, the task of offensive language detection on YouTube comments from the Dravidian lan- guages of Tamil, Malayalam and Kannada are seen upon as a mutliclass classification prob- lem. After being subjected to language spe- cific pre-processing, several Machine Learn- ing algorithms have been trained for the task at hand. The paper presents the accuracy results on the development datasets for all Machine Learning models that have been used and fi- nally presents the weighted average scores for the test set when using the best performing Ma- chine Learning model.
professionals@DravidianLangTech-EACL2021: Malayalam Offensive Language Identification - A Minimalistic Approach
Authors: Srinath Nair, Dolton Fernandes
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

The submission is being made as a working note as part of the Offensive Language Identification in Dravidian Languages shared task. The proposed model “DrOLIC” uses IndicBERT and a simple 4-layered MLP to do the multiclass classification problem and we achieved an F1 score of 0.85 on the Malayalam dataset.
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian Languages
Authors: Konthala Yasaswini, Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Sajeetha Thavareesan, Bharathi Raja Chakravarthi
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Diversity, Language Identification, Transfer Learning

This paper demonstrates our work for the shared task on Offensive Language Identification in Dravidian Languages-EACL 2021. Offensive language detection in the various social media platforms was identified previously. But with the increase in diversity of users, there is a need to identify the offensive language in multilingual posts that are largely code-mixed or written in a non-native script. We approach this challenge with various transfer learning-based models to classify a given post or comment in Dravidian languages (Malayalam, Tamil, and Kannada) into 6 categories. The source codes for our systems are published.
Hypers@DravidianLangTech-EACL2021: Offensive language identification in Dravidian code-mixed YouTube Comments and Posts
Authors: Charangan Vasantharajan, Uthayasanker Thayasivam
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

Code-Mixed Offensive contents are used pervasively in social media posts in the last few years. Consequently, gained the significant attraction of the research community for identifying the different forms of such content (e.g., hate speech, and sentiments) and contributed to the creation of datasets. Most of the recent studies deal with high-resource languages (e.g., English) due to many publicly available datasets, and by the lack of dataset in low-resource anguages, those studies are slightly involved in these languages. Therefore, this study has the focus on offensive language identification on code-mixed low-resourced Dravidian languages such as Tamil, Kannada, and Malayalam using the bidirectional approach and fine-tuning strategies. According to the leaderboard, the proposed model got a 0.96 F1-score for Malayalam, 0.73 F1-score for Tamil, and 0.70 F1-score for Kannada in the bench-mark. Moreover, in the view of multilingual models, this modal ranked 3rd and achieved favorable results and confirmed the model as the best among all systems submitted to these shared tasks in these three languages.
HUB@DravidianLangTech-EACL2021: Identify and Classify Offensive Text in Multilingual Code Mixing in Social Media
Authors: Bo Huang, Yang Bai
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Classification, Language Identification

This paper introduces the system description of the HUB team participating in DravidianLangTech - EACL2021: Offensive Language Identification in Dravidian Languages. The theme of this shared task is the detection of offensive content in social media. Among the known tasks related to offensive speech detection, this is the first task to detect offensive comments posted in social media comments in the Dravidian language. The task organizer team provided us with the code-mixing task data set mainly composed of three different languages: Malayalam, Kannada, and Tamil. The tasks on the code mixed data in these three different languages can be seen as three different comment/post-level classification tasks. The task on the Malayalam data set is a five-category classification task, and the Kannada and Tamil language data sets are two six-category classification tasks. Based on our analysis of the task description and task data set, we chose to use the multilingual BERT model to complete this task. In this paper, we will discuss our fine-tuning methods, models, experiments, and results.
HUB@DravidianLangTech-EACL2021: Meme Classification for Tamil Text-Image Fusion
Authors: Bo Huang, Yang Bai
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Meme Classification

This article describes our system for task DravidianLangTech - EACL2021: Meme classification for Tamil. In recent years, we have witnessed the rapid development of the Internet and social media. Compared with traditional TV and radio media platforms, there are not so many restrictions on the use of online social media for individuals and many functions of online social media platforms are free. Based on this feature of social media, it is difficult for people’s posts/comments on social media to be strictly and effectively controlled like TV and radio content. Therefore, the detection of negative information in social media has attracted attention from academic and industrial fields in recent years. The task of classifying memes is also driven by offensive posts/comments prevalent on social media. The data of the meme classification task is the fusion data of text and image information. To identify the content expressed by the meme, we develop a system that combines BiGRU and CNN. It can fuse visual features and text features to achieve the purpose of using multi-modal information from memetic data. In this article, we discuss our methods, models, experiments, and results.
ZYJ123@DravidianLangTech-EACL2021: Offensive Language Identification based on XLM-RoBERTa with DPCNN
Authors: Yingjia Zhao, Xin Tao
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

The development of online media platforms has given users more opportunities to post and comment freely, but the negative impact of offensive language has become increasingly apparent. It is very necessary for the automatic identification system of offensive language. This paper describes our work on the task of Offensive Language Identification in Dravidian language-EACL 2021. To complete this task, we propose a system based on the multilingual model XLM-Roberta and DPCNN. The test results on the official test data set confirm the effectiveness of our system. The weighted average F1-score of Kannada, Malayalam, and Tami language are 0.69, 0.92, and 0.76 respectively, ranked 6th, 6th, and 3rd
cs@DravidianLangTech-EACL2021: Offensive Language Identification Based On Multilingual BERT Model
Authors: Shi Chen, Bing Kong
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification, text-classification, Text Classification

This paper introduces the related content of the task “Offensive Language Identification in Dravidian LANGUAGES-EACL 2021”. The task requires us to classify Dravidian languages collected from social media into Not-Offensive, Off-Untargeted, Off-Target-Individual, etc. This data set contains actual annotations in code-mixed text posted by users on Youtube, not from the monolingual text in textbooks. Based on the features of the data set code mixture, we use multilingual BERT and TextCNN for semantic extraction and text classification. In this article, we will show the experiment and result analysis of this task.
CUSATNLP@DravidianLangTech-EACL2021:Language Agnostic Classification of Offensive Content in Tweets
Authors: Sara Renjit, Sumam Mary Idicula
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification, Position, Sentence, Sentence Embedding, Sentence-Embedding

Identifying offensive information from tweets is a vital language processing task. This task concentrated more on English and other foreign languages these days. In this shared task on Offensive Language Identification in Dravidian Languages, in the First Workshop of Speech and Language Technologies for Dravidian Languages in EACL 2021, the aim is to identify offensive content from code mixed Dravidian Languages Kannada, Malayalam, and Tamil. Our team used language agnostic BERT (Bidirectional Encoder Representation from Transformers) for sentence embedding and a Softmax classifier. The language-agnostic representation based classification helped obtain good performance for all the three languages, out of which results for the Malayalam language are good enough to obtain a third position among the participating teams.
IIIT_DWD@EACL2021: Identifying Troll Meme in Tamil using a hybrid deep learning approach
Authors: Ankit Kumar Mishra, Sunil Saumya
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Meme Classification

Social media are an open forum that allows people to share their knowledge, abilities, talents, ideas, or expressions. Simultaneously, it also allows people to post disrespectful, trolling, defamation, or negative content targeting users or the community based on their gender, race, religious beliefs, etc. Such posts are available in the form of text, image, video, and meme. Among them, memes are currently widely used to disseminate offensive material amongst people. It is primarily in the form of pictures and text. In the present paper, troll memes are identified, which is necessary to create a healthy society. To do so, a hybrid deep learning model combining convolutional neural networks and bidirectional long short term memory is proposed to identify trolled memes. The dataset used in the study is a part of the competition EACL 2021: Troll Meme classification in Tamil. The proposed model obtained 10th rank in the competition and reported a precision of 0.52, recall 0.59, and weighted F10.3.
Amrita_CEN_NLP@DravidianLangTech-EACL2021: Deep Learning-based Offensive Language Identification in Malayalam, Tamil and Kannada
Authors: Sreelakshmi K, Premjith B, Soman Kp
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

This paper describes the submission of the team Amrita_CEN_NLP to the shared task on Offensive Language Identification in Dravidian Languages at EACL 2021. We implemented three deep neural network architectures such as a hybrid network with a Convolutional layer, a Bidirectional Long Short-Term Memory network (Bi-LSTM) layer and a hidden layer, a network containing a Bi-LSTM and another with a Bidirectional Recurrent Neural Network (Bi-RNN). In addition to that, we incorporated a cost-sensitive learning approach to deal with the problem of class imbalance in the training data. Among the three models, the hybrid network exhibited better training performance, and we submitted the predictions based on the same.
IRLAB-DAIICT@DravidianLangTech-EACL2021: Neural Machine Translation
Authors: Raj Prajapati, Vedant Vijay Parikh, Prasenjit Majumder
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Machine Translation, Translation

This paper describes our team’s submission of the EACL DravidianLangTech-2021’s shared task on Machine Translation of Dravidian languages.We submitted our translations for English to Malayalam , Tamil , Telugu and also Tamil-Telugu language pairs. The submissions mainly focus on having adequate amount of data backed up by good preprocessing of it to produce quality translations,which includes some custom made rules to remove unnecessary sentences. We conducted several experiments on these models by tweaking the architecture,Byte Pair Encoding (BPE) and other hyperparameters.
IRNLP_DAIICT@DravidianLangTech-EACL2021:Offensive Language identification in Dravidian Languages using TF-IDF Char N-grams and MuRIL
Authors: Bhargav Dave, Shripad Bhat, Prasenjit Majumder
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Information Retrieval, Language Identification, Retrieval

This paper presents the participation of the IRNLPDAIICT team from Information Retrieval and Natural Language Processing lab at DA-IICT, India in DravidianLangTech-EACL2021 Offensive Language identification in Dravidian Languages. The aim of this shared task is to identify Offensive Language from a code-mixed data-set of YouTube comments. The task is to classify comments into Not Offensive (NO), Offensive Untargetede(OU), Offensive Targeted Individual (OTI), Offensive Targeted Group (OTG), Offensive Targeted Others (OTO), Other Language (OL) for three Dravidian languages: Kannada, Malayalam and Tamil. We use TF-IDF character n-grams and pretrained MuRIL embeddings for text representation and Logistic Regression and Linear SVM for classification. Our best approach achieved Ninth, Third and Eighth with weighted F1 score of 0.64, 0.95 and 0.71in Kannada, Malayalam and Tamil on test dataset respectively.
TrollMeta@DravidianLangTech-EACL2021: Meme classification using deep learning
Authors: Manoj Balaji J, Chinmaya Hs
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Classification, Deep Learning, Meme Classification

Memes act as a medium to carry one’s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes and neutral ones. The work involved classifying the images into offensive and non-offensive ones. The system implements resnet-50, a deep residual neural network architecture.
Maoqin @ DravidianLangTech-EACL2021: The Application of Transformer-Based Model
Authors: Maoqin Yang
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Modeling, Language Modelling

This paper describes the result of team-Maoqin at DravidianLangTech-EACL2021. The provided task consists of three languages(Tamil, Malayalam, and Kannada), I only participate in one of the language task-Malayalam. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages (Tamil-English, Malayalam-English, and Kannada-English) collected from social media. This is a classification task at the comment/post level. Given a Youtube comment, systems have to classify it into Not-offensive, Offensive-untargeted, Offensive-targeted-individual, Offensive-targeted-group, Offensive-targeted-other, or Not-in-indented-language. I use the transformer-based language model with BiGRU-Attention to complete this task. To prove the validity of the model, I also use some other neural network models for comparison. And finally, the team ranks 5th in this task with a weighted average F1 score of 0.93 on the private leader board.
Simon @ DravidianLangTech-EACL2021: Meme Classification for Tamil with BERT
Authors: Qinyu Que
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Classification, Meme Classification

In this paper, we introduce the system for the task of meme classification for Tamil, submitted by our team. In today’s society, social media has become an important platform for people to communicate. We use social media to share information about ourselves and express our views on things. It has gradually developed a unique form of emotional expression on social media – meme. The meme is an expression that is often ironic. This also gives the meme a unique sense of humor. But it’s not just positive content on social media. There’s also a lot of offensive content. Meme’s unique expression makes it often used by some users to post offensive content. Therefore, it is very urgent to detect the offensive content of the meme. Our team uses the natural language processing method to classify the offensive content of the meme. Our team combines the BERT model with the CNN to improve the model’s ability to collect statement information. Finally, the F1-score of our team in the official test set is 0.49, and our method ranks 5th.
Bitions@DravidianLangTech-EACL2021: Ensemble of Multilingual Language Models with Pseudo Labeling for offence Detection in Dravidian Languages
Authors: Debapriya Tula, Prathyush Potluri, Shreyas Ms, Sumanth Doddapaneni, Pranjal Sahu, Rohan Sukumaran, Parth Patwa
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4

With the advent of social media, we have seen a proliferation of data and public discourse. Unfortunately, this includes offensive content as well. The problem is exacerbated due to the sheer number of languages spoken on these platforms and the multiple other modalities used for sharing offensive content (images, gifs, videos and more). In this paper, we propose a multilingual ensemble-based model that can identify offensive content targeted against an individual (or group) in low resource Dravidian language. Our model is able to handle code-mixed data as well as instances where the script used is mixed (for instance, Tamil and Latin). Our solution ranked number one for the Malayalam dataset and ranked 4th and 5th for Tamil and Kannada, respectively.