1 code implementation • EMNLP 2021 • Rexhina Blloshmi, Tommaso Pasini, Niccolò Campolungo, Somnath Banerjee, Roberto Navigli, Gabriella Pasi
With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably.
1 code implementation • 25 Feb 2024 • Somnath Banerjee, Avik Dutta, Aaditya Agrawal, Rima Hazra, Animesh Mukherjee
With the AI revolution in place, the trend for building automated systems to support professionals in different domains such as the open source software systems, healthcare systems, banking systems, transportation systems and many others have become increasingly prominent.
1 code implementation • 23 Feb 2024 • Somnath Banerjee, Sayan Layek, Rima Hazra, Animesh Mukherjee
We query a series of LLMs -- Llama-2-13b, Llama-2-7b, Mistral-V2 and Mistral 8X7B -- and ask them to generate both text and instruction-centric responses.
no code implementations • 22 Feb 2024 • Somnath Banerjee, Maulindu Sarkar, Punyajoy Saha, Binny Mathew, Animesh Mukherjee
Second, in a dataset extension exercise, using influence functions to automatically identify data points that have been initially `silver' annotated by some existing method and need to be cross-checked (and corrected) by annotators to improve the model performance.
no code implementations • 23 Jan 2024 • Somnath Banerjee, Amruit Sahoo, Sayan Layek, Avik Dutta, Rima Hazra, Animesh Mukherjee
In the continuously advancing AI landscape, crafting context-rich and meaningful responses via Large Language Models (LLMs) is essential.
1 code implementation • 19 Jan 2024 • Rima Hazra, Sayan Layek, Somnath Banerjee, Soujanya Poria
In the rapidly advancing field of artificial intelligence, the concept of Red-Teaming or Jailbreaking large language models (LLMs) has emerged as a crucial area of study.
no code implementations • 9 Dec 2023 • Somnath Banerjee, Avik Dutta, Sayan Layek, Amruit Sahoo, Sam Conrad Joyce, Rima Hazra
In this paper, we delve into the advancement of domain-specific Large Language Models (LLMs) with a focus on their application in software development.
no code implementations • 12 Sep 2023 • Rima Hazra, Agnik Saha, Somnath Banerjee, Animesh Mukherjee
Community Question Answering (CQA) platforms steadily gain popularity as they provide users with fast responses to their queries.
no code implementations • 10 Sep 2023 • Rima Hazra, Debanjan Saha, Amruit Sahoo, Somnath Banerjee, Animesh Mukherjee
To facilitate the task of the moderators, in this work, we have tackled two significant issues for the askubuntu CQA platform: (1) retrieval of duplicate questions given a new question and (2) duplicate question confirmation time prediction.
Community Question Answering Duplicate-Question Retrieval +1
1 code implementation • 7 Oct 2022 • Mithun Das, Somnath Banerjee, Punyajoy Saha, Animesh Mukherjee
To overcome the existing research's limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets.
1 code implementation • 26 Apr 2022 • Mithun Das, Somnath Banerjee, Animesh Mukherjee
In this paper, to bridge the gap, we demonstrate a large-scale analysis of multilingual abusive speech in Indic languages.
no code implementations • DravidianLangTech (ACL) 2022 • Mithun Das, Somnath Banerjee, Animesh Mukherjee
We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022.
1 code implementation • 27 Nov 2021 • Mithun Das, Somnath Banerjee, Punyajoy Saha
In this FIRE 2021 shared task - "HASOC- Abusive and Threatening language detection in Urdu" the organizers propose an abusive language detection dataset in Urdu along with threatening language detection.
no code implementations • 27 Nov 2021 • Somnath Banerjee, Maulindu Sarkar, Nancy Agrawal, Punyajoy Saha, Mithun Das
Hate speech is considered to be one of the major issues currently plaguing online social media.
no code implementations • SEMEVAL 2020 • Somnath Banerjee, Sahar Ghannay, Sophie Rosset, Anne Vilnat, Paolo Rosso
This paper describes the participation of LIMSI{\_}UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text.
no code implementations • 31 Aug 2020 • Somnath Banerjee, Sudip Kumar Naskar, Paolo Rosso, Sivaji Bandyopadhyay
Overall, the stacking approach produces the best results for fine-grained classification and achieves 87. 79% of accuracy.
1 code implementation • 30 Aug 2020 • Somnath Banerjee, Sahar Ghannay, Sophie Rosset, Anne Vilnat, Paolo Rosso
This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text.
1 code implementation • SEMEVAL 2019 • Preeti Mukherjee, Mainak Pal, Somnath Banerjee, Sudip Kumar Naskar
This paper describes our system submissions as part of our participation (team name: JU{\_}ETCE{\_}17{\_}21) in the SemEval 2019 shared task 6: {``}OffensEval: Identifying and Catego- rizing Offensive Language in Social Media{''}.
no code implementations • IJCNLP 2017 • Somnath Banerjee, Partha Pakray, Riyanka Manna, Dipankar Das, Alex Gelbukh, er
In this paper, we describe a deep learning framework for analyzing the customer feedback as part of our participation in the shared task on Customer Feedback Analysis at the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017).