15 citations · 53 across the 48 of their papers we have counts for
18 papers · 1 filter
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…
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP
Chandra Kiran Reddy Evuru, Sreyan Ghosh, Sonal Kumar +3
We present CoDa (Constrained Generation based Data Augmentation), a controllable, effective, and training-free data augmentation technique for low-resource (data-scarce) NLP. Our 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…
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…
BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER
Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar +1
Biomedical Named Entity Recognition (BioNER) is the fundamental task of identifying named entities from biomedical text. However, BioNER suffers from severe data scarcity and lacks…
A novel multimodal dynamic fusion network for disfluency detection in spoken utterances
Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar +2
Disfluency, though originating from human spoken utterances, is primarily studied as a uni-modal text-based Natural Language Processing (NLP) task. Based on early-fusion and self-a…
CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representations
Vasista Sai Lodagala, Sreyan Ghosh, S. Umesh
While Self-Supervised Learning has helped reap the benefit of the scale from the available unlabeled data, the learning paradigms are continuously being bettered. We present a new…
Span Classification with Structured Information for Disfluency Detection in Spoken Utterances
Sreyan Ghosh, Sonal Kumar, Yaman Kumar Singla +2
Existing approaches in disfluency detection focus on solving a token-level classification task for identifying and removing disfluencies in text. Moreover, most works focus on leve…
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition
Ashish Seth, Lodagala V S V Durga Prasad, Sreyan Ghosh +1
Self-supervised learning (SSL) to learn high-level speech representations has been a popular approach to building Automatic Speech Recognition (ASR) systems in low-resource setting…
PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations
Lodagala V S V Durga Prasad, Sreyan Ghosh, S. Umesh
While self-supervised speech representation learning (SSL) models serve a variety of downstream tasks, these models have been observed to overfit to the domain from which the unlab…
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…
Leveraging Transformers for Hate Speech Detection in Conversational Code-Mixed Tweets
Zaki Mustafa Farooqi, Sreyan Ghosh, Rajiv Ratn Shah
In the current era of the internet, where social media platforms are easily accessible for everyone, people often have to deal with threats, identity attacks, hate, and bullying du…
DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances
Sreyan Ghosh, Samden Lepcha, S Sakshi +2
Toxic speech, also known as hate speech, is regarded as one of the crucial issues plaguing online social media today. Most recent work on toxic speech detection is constrained to t…
Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments
Sreyan Ghosh, Sonal Kumar
Social network platforms are generally used to share positive, constructive, and insightful content. However, in recent times, people often get exposed to objectionable content lik…
Cisco at AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides using Contextualized Embeddings
Sreyan Ghosh, Sonal Kumar, Harsh Jalan +2
This paper describes our proposed system for the AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides. In this specific task, given the contents of a slide we are ask…
End-to-end Named Entity Recognition from English Speech
Hemant Yadav, Sreyan Ghosh, Yi Yu +1
Named entity recognition (NER) from text has been a widely studied problem and usually extracts semantic information from text. Until now, NER from speech is mostly studied in a tw…