56 citations · 83 across the 10 of their papers we have counts for
10 papers
PARSE: LLM Driven Schema Optimization for Reliable Entity Extraction
Anubhav Shrimal, Aryan Jain, Soumyajit Chowdhury +1
Structured information extraction from unstructured text is critical for emerging Software 3.0 systems where LLM agents autonomously interact with APIs and tools. Recent approaches…
MARCO: Multi-Agent Real-time Chat Orchestration
Anubhav Shrimal, Stanley Kanagaraj, Kriti Biswas +4
Large language model advancements have enabled the development of multi-agent frameworks to tackle complex, real-world problems such as to automate tasks that require interactions…
MARS: Multilingual Aspect-centric Review Summarisation
Sandeep Sricharan Mukku, Abinesh Kanagarajan, Chetan Aggarwal +1
Summarizing customer feedback to provide actionable insights for products/services at scale is an important problem for businesses across industries. Lately, the review volumes are…
InsightNet: Structured Insight Mining from Customer Feedback
Sandeep Sricharan Mukku, Manan Soni, Jitenkumar Rana +4
We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome…
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension
Anubhav Shrimal, Avi Jain, Kartik Mehta +1
NER has been traditionally formulated as a sequence labeling task. However, there has been recent trend in posing NER as a machine reading comprehension task (Wang et al., 2020; Me…
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue
Gaurav Kumar, Rishabh Joshi, Jaspreet Singh +1
The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take car…
Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition
Suraj Tripathi, Abhay Kumar, Abhiram Ramesh +2
This paper proposes a Residual Convolutional Neural Network (ResNet) based on speech features and trained under Focal Loss to recognize emotion in speech. Speech features such as S…
Deep Learning based Emotion Recognition System Using Speech Features and Transcriptions
Suraj Tripathi, Abhay Kumar, Abhiram Ramesh +2
This paper proposes a speech emotion recognition method based on speech features and speech transcriptions (text). Speech features such as Spectrogram and Mel-frequency Cepstral Co…
Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition
Suraj Tripathi, Abhiram Ramesh, Abhay Kumar +2
This paper proposes a Convolutional Neural Network (CNN) inspired by Multitask Learning (MTL) and based on speech features trained under the joint supervision of softmax loss and c…
A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks
Tushar Semwal, Gaurav Mathur, Promod Yenigalla +1
Transfer Learning (TL) plays a crucial role when a given dataset has insufficient labeled examples to train an accurate model. In such scenarios, the knowledge accumulated within a…