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Found 19 papers, 8 papers with code
Cluster Analysis of Online Mental Health Discourse using Topic-Infused Deep Contextualized Representations
With mental health as a problem domain in NLP, the bulk of contemporary literature revolves around building better mental illness prediction models.
Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes
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.
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data.
Multitask Learning Can Improve Worst-Group Outcomes
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.
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model
Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills.
Adapting the adapters for code-switching in multilingual ASR
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)
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.
Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment
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.
SciFix: Outperforming GPT3 on Scientific Factual Error Correction
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
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
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.
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?
A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities.
Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter
The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation.
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues
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
VLSI Systems for signal processing and Communications
The growing advances in VLSI technology and design tools have exponentially expanded the application domain of digital signal processing over the past 10 years.
L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset
We also present the guidelines using which we annotated the tweets.
PVG at WASSA 2021: A Multi-Input, Multi-Task, Transformer-Based Architecture for Empathy and Distress Prediction
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
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.
Experimental Evaluation of Deep Learning models for Marathi Text Classification
The Marathi language is one of the prominent languages used in India.
