Table of Contents

• Preface [doi]
• Overview of the FIRE 2023 Track: Artificial Intelligence on Social Media (AISoMe)Moumita Basu, Soham Poddar, Moumita Basu, Saptarshi Ghosh 0001, Kripabandhu Ghosh. 1-5 [doi]
• Enhancing Multilabel Classification of Anti-Vaccine Tweets with the COVID-Twitter-BERTAritra Mandal. 6-11 [doi]
• Vaccine Vision: A deep learning approach towards identifying societal concerns regarding vaccinesKaustav Das. 12-17 [doi]
• Multi-Label Classification of COVID-Tweets Using Large Language ModelsAniket Deroy, Subhankar Maity. 18-23 [doi]
• Tweet Classifier: Advancements in Multi-Label AnalysisSwastik Anupam. 24-29 [doi]
• Multi-label Classification of Covid-19 Vaccine TweetSumit Das, Palvika Bansal, Vikas Rai, Shalini Kumari. 30-43 [doi]
• VaxTweetClassifier: BERT for Dealing with Vaccination TweetsShankha S. Das, Sohan Choudhury, Priyam Saha, Dipankar Das. 44-51 [doi]
• VaxVerdict: a RoBERTa Based Multilabel Tweet ClassifierAditya O. Patil, Raj Awate, Shivakumar Ranade, Sheetal Sonawane. 52-58 [doi]
• VaxiBERT: A BERT-Based Classifier for Vaccine Tweets with Multi-Label AnnotationsRajat Singh, Shivangi Bithel, Samidha Verma, Prachi. 59-66 [doi]
• Deciphering Vaccine Sentiments: Transformer Models in Social Media AnalysisShriram M. S. 67-74 [doi]
• BERT-Powered Multi-label Classifier: Analyzing Public COVID Vaccination DiscourseRanjit Patro, Asutosh Mishra. 75-81 [doi]
• Analysing Crowd-Sourced Vaccine Data Using Machine Learning: Uncovering Concerns and InsightsLakshmi Gopal. 82-90 [doi]
• Unveiling Diverse Vaccine Sentiments: Multi-Label Text ClassificationK. Shanmukha Naveen, S. Sharon Roshini, S. Karthika. 91-98 [doi]
• Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social MediaSomsubhra De, Shaurya Vats. 99-111 [doi]
• TweetClass: COVID-19 Vaccine Tweet Classification with scikit-learnBaivab Chakraborty, Subhajit Srimani, Souvit Biswas. 112-117 [doi]
• Overview of the CIRAL Track at FIRE 2023: Cross-lingual Information Retrieval for African LanguagesMofetoluwa Adeyemi, Akintunde Oladipo, Xinyu Crystina Zhang, David Alfonso-Hermelo, Mehdi Rezagholizadeh, Boxing Chen, Jimmy Lin. 118-136 [doi]
• Extending Translate-Train for ColBERT-X to African Language CLIREugene Yang, Dawn J. Lawrie, Paul McNamee, James Mayfield. 137-146 [doi]
• Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through Claim Detection and Identification of Claim SpansMegha Sundriyal, Md. Shad Akhtar, Tanmoy Chakraborty 0002. 147-158 [doi]
• Current language models' poor performance on pragmatic aspects of natural languageAlbert Pritzkau, Julia Waldmüller, Olivier Blanc, Michaela Geierhos, Ulrich Schade. 159-169 [doi]
• Positional Transformers for Claim Span IdentificationMichael Sullivan, Navid Madani, Sougata Saha, Rohini K. Srihari. 170-178 [doi]
• Overview of CoLI-Tunglish: Word-level Language Identification in Code-mixed Tulu Text at FIRE 2023Asha Hegde, Fazlourrahman Balouchzahi, Sharal Coelho, H. L. Shashirekha, Hamada A. Nayel, Sabur Butt. 179-190 [doi]
• Using Character Ngrams for Word-Level Language Identification in Trilingual Code-Mixed Data (and Even More)Yves Bestgen. 191-197 [doi]
• Word-Level Language Identification of Code-Mixed Tulu-English DataPoorvi Shetty. 198-204 [doi]
• BFCAI at CoLI-Tunglish@FIRE 2023: Machine Learning Based Model for Word-level Language Identification in Code-mixed Tulu TextsAhmed M. Fetouh, Hamada Nayel. 205-212 [doi]
• Word-level Language Identification in Code-mixed Tulu TextsSushma N, Asha Hegde, Hosahalli Lakshmaiah Shashirekha. 213-222 [doi]
• Advancing Language Identification in Code-Mixed Tulu Texts: Harnessing Deep Learning TechniquesSupriya Chanda, Anshika Mishra, Sukomal Pal. 223-230 [doi]
• Overview of Sarcasm Identification of Dravidian Languages in DravidianCodeMix@FIRE-2023Bharathi Raja Chakravarthi, Sripriya N, Bharathi B, Nandhini K, Subalalitha Chinnaudayar Navaneethakrishnan, Thenmozhi Durairaj, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Kishore Kumar Ponnusamy, Charmathi Rajkumar. 231-239 [doi]
• Sarcasm Identification in Dravidian Languages Tamil and MalayalamPoorvi Shetty. 240-248 [doi]
• Sarcasm Detection in Dravidian Code-Mixed Text Using Transformer-Based ModelsAnik Basu Bhaumik, Mithun Das. 249-258 [doi]
• Cross-Linguistic Sarcasm Detection in Tamil and Malayalam: A Multilingual ApproachDhanya Krishnan, Krithika Dharanikota, B. Bharathi. 259-269 [doi]
• Unmasking Sarcasm: Sarcastic Language Detection with BiLSTMsAnusha M. D, Parameshwar R. Hegde. 270-277 [doi]
• Identifying the Type of Sarcasm in Dravidian Languages using Deep-Learning ModelsRamya Sivakumar, Jerin Mahibha C, B. Monica Jenefer. 278-286 [doi]
• Learning Models with Text Augmentation for Sarcasm Detection in Malayalam and Tamil Code-mixed TextsNavya N, Vanitha V, Asha Hegde, H. L. Shashirekha. 287-298 [doi]
• A Few Shot Learning to Detect Sarcasm in Tamil and Malayalam Code Mixed DataShanmitha Thirumoorthy, Manavh N. R, Durairaj Thenmozhi, Ratnavel Rajalakshmi. 299-305 [doi]
• Sarcasm Detection in Dravidian Languages using Transformer ModelsMadhumitha. M, Kunguma Akshatra. M, Tejashri. J, C. Jerin Mahibha, Durairaj Thenmozhi. 306-318 [doi]
• Sarcasm Identification in Codemix Dravidian LanguagesPrabhu Ram. N, Meera Devi. T, Kanisha. V, Meharnath. S, Manoji. B. 319-326 [doi]
• Sarcasm Identification Of Dravidian Languages (Malayalam and Tamil)V. Indirakanth, Dharunkumar Udayakumar, Thenmozhi Durairaj, B. Bharathi. 327-335 [doi]
• Sarcasm Detection in Tamil and Malayalam Dravidian Code-Mixed TextSupriya Chanda, Anshika Mishra, Sukomal Pal. 336-343 [doi]
• Overview of the HASOC Subtrack at FIRE 2023: Hate-Speech Identification in Sinhala and GujaratiShrey Satapara, Hiren Madhu, Tharindu Ranasinghe, Alphaeus Eric Dmonte, Marcos Zampieri, Pavan Pandya, Nisarg Shah, Sandip Modha, Prasenjit Majumder, Thomas Mandl 0001. 344-350 [doi]
• Overview of the HASOC Subtrack at FIRE 2023: Identification of Conversational Hate-SpeechHiren Madhu, Shrey Satapara, Pavan Pandya, Nisarg Shah, Thomas Mandl 0001, Sandip Modha. 351-359 [doi]
• Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span DetectionSarah Masud, Mohammad Aflah Khan, Md. Shad Akhtar, Tanmoy Chakraborty. 360-367 [doi]
• Annihilate Hates (Task 4 HASOC 2023): Hate Speech Detection in Assamese Bengali and Bodo languagesKoyel Ghosh, Apurbalal Senapati, Aditya Shankar Pal. 368-382 [doi]
• Hate and Offensive Content Identification in Indo-Aryan Languages using Transformer-based ModelsOlumide Ebenezer Ojo, Olaronke Oluwayemisi Adebanji, Hiram Calvo, Alexander F. Gelbukh, Anna Feldman, Grigori Sidorov. 383-392 [doi]
• Addressing Hate Speech: ATLANTIS for Efficient Hate Span DetectionNiyar R. Barman, Krish Sharma, Yashraj Poddar, Advaitha Vetagiri, Partha Pakray. 393-402 [doi]
• Detecting Hate Speech and Offensive Content in English and Indo-Aryan TextsMohammadmostafa Rostamkhani, Sauleh Eetemadi. 403-410 [doi]
• Using Only Character Ngrams for Hate Speech and Offensive Content Identification in Five Low-Ressource LanguagesYves Bestgen. 411-417 [doi]
• Detecting Offensive Language in Bengali Bodo and Assamese using Word Unigrams Char N-grams Classical Machine Learning and Deep Learning MethodsAvigail Stekel, Avital Prives, Yaakov HaCohen-Kerner. 418-426 [doi]
• Harnessing Pre-Trained Sentence Transformers for Offensive Language Detection in Indian LanguagesAnanya Joshi 0002, Raviraj Joshi. 427-434 [doi]
• Enhancing Hate Speech Detection in Sinhala and Gujarati: Leveraging BERT Models and Linguistic ConstraintsG. Gnana Sai, Aswath Venkatesh, Kishore N, Olirva M, Balaji V. A, Prabavathy Balasundaram. 435-444 [doi]
• Multilingual Hate Speech and Offensive Language Detection of Low Resource LanguagesAbhinav Reddy Gutha, Nidamanuri Sai Adarsh, Ananya Alekar, Dinesh Reddy. 445-458 [doi]
• Breaking Barriers: Multilingual Toxicity Analysis for Hate Speech and Offensive Language in Low-Resource Indo-Aryan LanguagesCh Muhammad Awais, Jayveersinh Raj. 459-473 [doi]
• Examining Hate Speech Detection Across Multiple Indo-Aryan Languages in Tasks 1 & 4Gyandeep Kalita, Eisha Halder, Chetna Taparia, Advaitha Vetagiri, Partha Pakray. 474-485 [doi]
• Crossing Borders: Multilingual Hate Speech DetectionSupriya Chanda, Abhishek Dhaka, Sukomal Pal. 486-500 [doi]
• Hate speech classification for Sinhalese and GujaratiMuhammad Deedahwar Mazhar Qureshi, Madhuri Sawant, Muhammad Atif Qureshi, Wael Rashwan, Arjumand Younus, Simon Caton. 501-515 [doi]
• Hate Speech Detection in Low Resource Indo-Aryan LanguagesSougata Saha, Michael Sullivan, Rohini K. Srihari. 516-520 [doi]
• Sinhala and Gujarati Hate Speech DetectionM. Krithik Sathya, K. H. Gopalakrishnan, Manickam PA, Prabavathy Balasundaram. 521-531 [doi]
• Cross-Linguistic Offensive Language Detection: BERT-Based Analysis of Bengali Assamese & Bodo Conversational Hateful Content from Social MediaJhuma Kabir Mim, Mourad Oussalah 0002, Akash Singhal. 532-543 [doi]
• Improving Detection of Hate Speech, Offensive Language and Profanity in Short Texts with SVM ClassifierSurya Agustian, Zaky Idhafi, Agit Fadillah Rihardi. 544-552 [doi]
• Bengali Hate Speech Detection Using Deep Learning TechniqueChandan Senapati, Utpal Roy. 553-562 [doi]
• Taming Toxicity: Learning Models for Hate Speech and Offensive Language Detection in Social Media TextPrajnashree M, Rachana K, Asha Hegde, Kavya Girish, Sharal Coelho, H. L. Shashirekha. 563-573 [doi]
• Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and TransformersNikhil Narayan, Mrutyunjay Biswal, Pramod Goyal, Abhranta Panigrahi. 574-587 [doi]
• Multilingual Hate Speech Detection Using Ensemble of Transformer ModelsMd Saroar Jahan, Fadi Hassan, Walid Aransa, Abdessalam Bouchekif. 588-597 [doi]
• Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023Srijoni Majumdar, Soumen Paul, Bhargav Dave, Debjyoti Paul, Ayan Bandyopadhyay, Samiran Chattopadhyay, Partha Pratim Das, Paul D. Clough, Prasenjit Majumder. 598-604 [doi]
• A ML-LLM pairing for better code comment classificationHanna Abi Akl. 605-614 [doi]
• Software Metadata Classification based on Generative Artificial IntelligenceSeetharam Killivalavan, Durairaj Thenmozhi. 615-623 [doi]
• Leveraging Generative AI: Improving Software Metadata Classification with Generated Code-Comment PairsSamah Syed, Angel Deborah S. 624-632 [doi]
• Source Code Comment Classification using machine learning algorithmsTrisha Datta. 633-641 [doi]
• Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved AccuracyRohith Arumugam S, Angel Deborah S. 642-651 [doi]
• Binary Classification of Source Code Comments using Machine Learning ModelsLisa Sarkar. 652-660 [doi]
• A study of the impact of generative AI-based data augmentation on software metadata classificationTripti Kumari, Chakali Sai Charan, Ayan Das 0004. 661-669 [doi]
• Leveraging Language Models for Code Comment ClassificationJagrat T. Patel. 670-678 [doi]
• Enhancing Code Comment Classification Using Language ModelsJaivin Barot. 679-688 [doi]
• Assessing the Utility of C Comments with SVM and Naïve Bayes ClassifierAritra Mitra. 689-695 [doi]
• Source Code Comment Classification using Naive Bayes and Support Vector MachineRaj Jitendra Shah. 696-703 [doi]
• On the Impact of Synthetic Data on Code Comment Usefulness PredictionVishesh Agarwal. 704-710 [doi]
• Exploring LLM-based Data Augmentation Techniques for Code Comment Quality ClassificationPriyam Dalmia. 711-717 [doi]
• Exploring Large Language Models for Code ExplanationPaheli Bhattacharya, Manojit Chakraborty, Kartheek N. S. N. Palepu, Vikas Pandey, Ishan Dindorkar, Rakesh Rajpurohit, Rishabh Gupta. 718-723 [doi]
• Key Takeaways from the Second Shared Task on Indian Language Summarization (ILSUM 2023)Shrey Satapara, Parth Mehta 0001, Sandip Modha, Debasis Ganguly. 724-733 [doi]
• Prompted Zero-Shot Multi-label Classification of Factual Incorrectness in Machine-Generated SummariesAniket Deroy, Subhankar Maity, Saptarshi Ghosh 0001. 734-746 [doi]
• Advancing Human-Like Summarization: Approaches to Text SummarizationSaliq Gowhar, Bhavya Sharma, Ashutosh K. Gupta, Anand Kumar Madasamy. 747-754 [doi]
• Named Entity-Aware Abstractive Text Summarization for Hindi LanguageSaumay Gupta, Sukomal Pal. 755-765 [doi]
• Text Summarization for Indian Languages: Finetuned Transformer Model ApplicationV. Ilanchezhiyan, R. Darshan, E. M. Milin Dhitshithaa, B. Bharathi. 766-774 [doi]
• Overview of MTIL Track at FIRE 2023: Machine Translation for Indian LanguagesSurupendu Gangopadhyay. 775-782 [doi]
• Hindi-Odia Machine Translation SystemRakesh Chandra Balabantaray. 783-789 [doi]
• Fine tuning based Domain Adaptation for Machine Translation of Low Resource Indic LanguagesAmulya Ratna Dash. 790-795 [doi]
• Bidirectional Hindi-Punjabi Machine TranslationMukund K. Roy. 796-801 [doi]