Search Results for author: Atharva Kulkarni

Found 19 papers, 8 papers with code

Cluster Analysis of Online Mental Health Discourse using Topic-Infused Deep Contextualized Representations

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.

Text Clustering

SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking

no code implementations3 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.

dialog state tracking Few-Shot Learning +2

Multitask Learning Can Improve Worst-Group Outcomes

1 code implementation5 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.


Adapting the adapters for code-switching in multilingual ASR

1 code implementation11 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

Exploring Different Time-series-Transformer (TST) Architectures: A Case Study in Battery Life Prediction for Electric Vehicles (EVs)

no code implementations7 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.

Decoder Management +1

Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment

1 code implementation1 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.

Benchmarking Hate Speech Detection

SciFix: Outperforming GPT3 on Scientific Factual Error Correction

1 code implementation24 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.

Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India

no code implementations27 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.

ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus

no code implementations28 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.

Speech Synthesis

Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?

no code implementations26 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.

Semantic Role Labeling

Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter

1 code implementation10 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.

Misinformation token-classification +1

When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues

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).

Sarcasm Detection

VLSI Systems for signal processing and Communications

no code implementations10 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.

PVG at WASSA 2021: A Multi-Input, Multi-Task, Transformer-Based Architecture for Empathy and Distress Prediction

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.

An Attention Ensemble Approach for Efficient Text Classification of Indian Languages

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.

General Classification Sentence +2

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