3 citations · 12 across the 10 of their papers we have counts for
10 papers
MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark
S Sakshi, Utkarsh Tyagi, Sonal Kumar +6
The ability to comprehend audio--which includes speech, non-speech sounds, and music--is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benc…
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions
Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar +4
We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a…
A Closer Look at the Limitations of Instruction Tuning
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar +5
Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trai…
DALE: Generative Data Augmentation for Low-Resource Legal NLP
Sreyan Ghosh, Chandra Kiran Evuru, Sonal Kumar +4
We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP. DALE addresses the challenges existing frameworks pose in generating effec…
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
Shivani Kumar, Ramaneswaran S, Md Shad Akhtar +1
Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable re…
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
Sreyan Ghosh, Ashish Seth, Sonal Kumar +7
A fundamental characteristic of audio is its compositional nature. Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representatio…
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
Sreyan Ghosh, Utkarsh Tyagi, Manan Suri +3
Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keywor…
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization
Shivam Sharma, Ramaneswaran S, Udit Arora +2
Memes are a powerful tool for communication over social media. Their affinity for evolving across politics, history, and sociocultural phenomena makes them an ideal communication v…
M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations
Sreyan Ghosh, S Ramaneswaran, Utkarsh Tyagi +4
Expression of emotions is a crucial part of daily human communication. Emotion recognition in conversations (ERC) is an emerging field of study, where the primary task is to identi…
MMER: Multimodal Multi-task Learning for Speech Emotion Recognition
Sreyan Ghosh, Utkarsh Tyagi, S Ramaneswaran +2
In this paper, we propose MMER, a novel Multimodal Multi-task learning approach for Speech Emotion Recognition. MMER leverages a novel multimodal network based on early-fusion and…