Search Results for author: Rishabh Gupta

Found 20 papers, 8 papers with code

Deep Learning Based Named Entity Recognition Models for Recipes

no code implementations27 Feb 2024 Mansi Goel, Ayush Agarwal, Shubham Agrawal, Janak Kapuriya, Akhil Vamshi Konam, Rishabh Gupta, Shrey Rastogi, Niharika, Ganesh Bagler

Simultaneously, we systematically cleaned and analyzed ingredient phrases from RecipeDB, the gold-standard recipe data repository, and annotated them using the Stanford NER.

named-entity-recognition Named Entity Recognition +2

Sparse Graph Representations for Procedural Instructional Documents

no code implementations6 Feb 2024 Shruti Singh, Rishabh Gupta

We propose two approaches to model document similarity by representing document pairs as a directed and sparse JCIG that incorporates sequential information.

Data-driven decision-focused surrogate modeling

1 code implementation23 Aug 2023 Rishabh Gupta, Qi Zhang

We introduce the concept of decision-focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real-time settings.

Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation

1 code implementation23 May 2023 Rishabh Gupta, Shaily Desai, Manvi Goel, Anil Bandhakavi, Tanmoy Chakraborty, Md. Shad Akhtar

Due to the complex and multifaceted nature of hate speech, utilizing multiple forms of counter-narratives with varying intents may be advantageous in different circumstances.

Speaker Profiling in Multiparty Conversations

no code implementations18 Apr 2023 Shivani Kumar, Rishabh Gupta, Md Shad Akhtar, Tanmoy Chakraborty

We have evaluated various baselines on this dataset and benchmarked it with a new neural model, SPOT, which we introduce in this paper.

Speaker Profiling valid

Dimension reduction and redundancy removal through successive Schmidt decompositions

no code implementations9 Feb 2023 Ammar Daskin, Rishabh Gupta, Sabre Kais

We show that data with distributions such as uniform, Poisson, exponential, or similar to these distributions can be approximated by using only a few terms which can be easily mapped onto quantum circuits.

Dimensionality Reduction

Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising

no code implementations6 Feb 2023 Rishabh Gupta, Venktesh V., Mukesh Mohania, Vikram Goyal

This allows for learning a denoising function that operates over both aspects and produces semantically equivalent and syntactically diverse outputs through grounded noise injection.

Denoising Memorization +1

A Privacy-Preserving Outsourced Data Model in Cloud Environment

no code implementations24 Nov 2022 Rishabh Gupta, Ashutosh Kumar Singh

Therefore, a privacy-preserving model is proposed, which protects the privacy of the data without compromising machine learning efficiency.

Cloud Computing Fraud Detection +2

Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization

no code implementations27 Oct 2022 Rishabh Gupta, Qi Zhang

In this work, we consider the inverse problem where we use prior decision data to uncover the underlying decision-making process in the form of a mathematical optimization model.

Decision Making

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

2 code implementations6 Dec 2021 Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Shrivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, Michael A. Yee, Jing Zhang, Yue Zhang

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.

Data Augmentation

Evaluation of deep learning models for multi-step ahead time series prediction

1 code implementation26 Mar 2021 Rohitash Chandra, Shaurya Goyal, Rishabh Gupta

The deep learning methods comprise simple recurrent neural networks, long short-term memory (LSTM) networks, bidirectional LSTM networks, encoder-decoder LSTM networks, and convolutional neural networks.

 Ranked #1 on Time Series Prediction on Sunspot (using extra training data)

Time Series Time Series Prediction

Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization

1 code implementation16 Sep 2020 Rishabh Gupta, Qi Zhang

This work addresses inverse linear optimization where the goal is to infer the unknown cost vector of a linear program.

Towards Semantic Noise Cleansing of Categorical Data based on Semantic Infusion

no code implementations6 Feb 2020 Rishabh Gupta, Rajesh N Rao

Based on this technique, we propose an unsupervised text-preprocessing framework to filter the semantic noise using the context of the terms.

Decision Making

DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services

1 code implementation1 Jun 2019 Deepak Thukral, Adesh Pandey, Rishabh Gupta, Vikram Goyal, Tanmoy Chakraborty

In this paper, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem.

Community Question Answering

Simple Features for Strong Performance on Named Entity Recognition in Code-Switched Twitter Data

no code implementations WS 2018 Devanshu Jain, Maria Kustikova, Mayank Darbari, Rishabh Gupta, Stephen Mayhew

In this work, we address the problem of Named Entity Recognition (NER) in code-switched tweets as a part of the Workshop on Computational Approaches to Linguistic Code-switching (CALCS) at ACL{'}18.

Language Identification named-entity-recognition +5

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