no code implementations • EACL (Louhi) 2021 • Atharva Kulkarni, Amey Hengle, Pradnya Kulkarni, Manisha Marathe
With mental health as a problem domain in NLP, the bulk of contemporary literature revolves around building better mental illness prediction models.
no code implementations • CONSTRAINT (ACL) 2022 • Shivam Sharma, Tharun Suresh, Atharva Kulkarni, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, Tanmoy Chakraborty
We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities.
no code implementations • 3 Feb 2024 • Atharva Kulkarni, Bo-Hsiang Tseng, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Hong Yu, Shruti Bhargava
Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data.
1 code implementation • 5 Dec 2023 • Atharva Kulkarni, Lucio Dery, Amrith Setlur, aditi raghunathan, Ameet Talwalkar, Graham Neubig
We primarily consider the standard setting of fine-tuning a pre-trained model, where, following recent work \citep{gururangan2020don, dery2023aang}, we multitask the end task with the pre-training objective constructed from the end task data itself.
no code implementations • 23 Oct 2023 • Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schütze, Kemal Oflazer, David R. Mortensen
Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills.
1 code implementation • 11 Oct 2023 • Atharva Kulkarni, Ajinkya Kulkarni, Miguel Couceiro, Hanan Aldarmaki
Recently, large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition (ASR) to many low-resource languages.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 7 Aug 2023 • Niranjan Sitapure, Atharva Kulkarni
In recent years, battery technology for electric vehicles (EVs) has been a major focus, with a significant emphasis on developing new battery materials and chemistries.
1 code implementation • 1 Jun 2023 • Atharva Kulkarni, Sarah Masud, Vikram Goyal, Tanmoy Chakraborty
Our proposed solution HEN-mBERT is a modular, multilingual, mixture-of-experts model that enriches the linguistic subspace with latent endogenous signals from history, topology, and exemplars.
1 code implementation • 24 May 2023 • Dhananjay Ashok, Atharva Kulkarni, Hai Pham, Barnabás Póczos
Our method outperforms the very LLM that was used to generate the annotated dataset -- with Few-Shot Prompting on GPT3. 5 achieving 58%, 61%, and 64% on the respective datasets, a consistently lower correction accuracy, despite using nearly 800 times as many parameters as our model.
no code implementations • 27 Apr 2023 • Atharva Kulkarni, Raya Das, Ravi S. Srivastava, Tanmoy Chakraborty
The proposed project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators.
no code implementations • 28 Feb 2023 • Ajinkya Kulkarni, Atharva Kulkarni, Sara Abedalmonem Mohammad Shatnawi, Hanan Aldarmaki
In a move towards filling this gap in resources, we present a speech corpus for Classical Arabic Text-to-Speech (ClArTTS) to support the development of end-to-end TTS systems for Arabic.
no code implementations • 26 Jan 2023 • Shivam Sharma, Atharva Kulkarni, Tharun Suresh, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, Tanmoy Chakraborty
A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities.
1 code implementation • 10 Oct 2022 • Megha Sundriyal, Atharva Kulkarni, Vaibhav Pulastya, Md Shad Akhtar, Tanmoy Chakraborty
The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation.
1 code implementation • ACL 2022 • Shivani Kumar, Atharva Kulkarni, Md Shad Akhtar, Tanmoy Chakraborty
In this work, we study the discourse structure of sarcastic conversations and propose a novel task - Sarcasm Explanation in Dialogue (SED).
Ranked #1 on Sarcasm Detection on WITS
no code implementations • 10 Jun 2021 • Aditya Kulkarni, Atharva Kulkarni, Ankit Lad, Laksh Maheshwari, Jayant Majji
The growing advances in VLSI technology and design tools have exponentially expanded the application domain of digital signal processing over the past 10 years.
1 code implementation • EACL (WASSA) 2021 • Atharva Kulkarni, Meet Mandhane, Manali Likhitkar, Gayatri Kshirsagar, Raviraj Joshi
We also present the guidelines using which we annotated the tweets.
1 code implementation • EACL (WASSA) 2021 • Atharva Kulkarni, Sunanda Somwase, Shivam Rajput, Manisha Marathe
Leveraging the textual data, demographic features, psychological test score, and the intrinsic interdependencies of primitive emotions and empathy, we propose a multi-input, multi-task framework for the task of empathy score prediction.
no code implementations • ICON 2020 • Atharva Kulkarni, Amey Hengle, Rutuja Udyawar
Experimental results show that the proposed model outperforms various baseline machine learning and deep learning models in the given task, giving the best validation accuracy of 89. 57\% and f1-score of 0. 8875.
no code implementations • 13 Jan 2021 • Atharva Kulkarni, Meet Mandhane, Manali Likhitkar, Gayatri Kshirsagar, Jayashree Jagdale, Raviraj Joshi
The Marathi language is one of the prominent languages used in India.