Search Results for author: Oishik Chatterjee

Found 8 papers, 4 papers with code

WARM: A Weakly (+Semi) Supervised Math Word Problem Solver

1 code implementation COLING 2022 Oishik Chatterjee, Isha Pandey, Aashish Waikar, Vishwajeet Kumar, Ganesh Ramakrishnan

In order to address this challenge of equation annotation, we propose a weakly supervised model for solving MWPs by requiring only the final answer as supervision.

Math

ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement

no code implementations12 Sep 2024 Oishik Chatterjee, Pooja Aggarwal, Suranjana Samanta, Ting Dai, Prateeti Mohapatra, Debanjana Kar, Ruchi Mahindru, Steve Barbieri, Eugen Postea, Brad Blancett, Arthur De Magalhaes

In the rapidly evolving landscape of site reliability engineering (SRE), the demand for efficient and effective solutions to manage and resolve issues in site and cloud applications is paramount.

Script Generation

CodeSift: An LLM-Based Reference-Less Framework for Automatic Code Validation

no code implementations28 Aug 2024 Pooja Aggarwal, Oishik Chatterjee, Ting Dai, Prateeti Mohapatra, Brent Paulovicks, Brad Blancett, Arthur De Magalhaes

The advent of large language models (LLMs) has greatly facilitated code generation, but ensuring the functional correctness of generated code remains a challenge.

Code Generation

Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce

no code implementations11 Aug 2022 Oishik Chatterjee, Jaidam Ram Tej, Narendra Varma Dasaraju

We propose a novel approach which can use information from customer reviews along with customer and product features for size and fit predictions.

Recommendation Systems

WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems

no code implementations14 Apr 2021 Oishik Chatterjee, Isha Pandey, Aashish Waikar, Vishwajeet Kumar, Ganesh Ramakrishnan

We approach this problem by first learning to generate the equation using the problem description and the final answer, which we subsequently use to train a supervised MWP solver.

Math

Semi-Supervised Data Programming with Subset Selection

1 code implementation Findings (ACL) 2021 Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer

The first contribution of this work is an introduction of a framework, \model which is a semi-supervised data programming paradigm that learns a \emph{joint model} that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space.

text-classification Text Classification

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