Mohit Gupta


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Image Manipulation via Multi-Hop Instructions - A New Dataset and Weakly-Supervised Neuro-Symbolic Approach
Harman Singh | Poorva Garg | Mohit Gupta | Kevin Shah | Ashish Goswami | Satyam Modi | Arnab Mondal | Dinesh Khandelwal | Dinesh Garg | Parag Singla
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

We are interested in image manipulation via natural language text – a task that is useful for multiple AI applications but requires complex reasoning over multi-modal spaces. We extend recently proposed Neuro Symbolic Concept Learning (NSCL), which has been quite effective for the task of Visual Question Answering (VQA), for the task of image manipulation. Our system referred to as NeuroSIM can perform complex multi-hop reasoning over multi-object scenes and only requires weak supervision in the form of annotated data for VQA. NeuroSIM parses an instruction into a symbolic program, based on a Domain Specific Language (DSL) comprising of object attributes and manipulation operations, that guides its execution. We create a new dataset for the task, and extensive experiments demonstrate that NeuroSIM is highly competitive with or beats SOTA baselines that make use of supervised data for manipulation.


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Adjective Intensity and Sentiment Analysis
Raksha Sharma | Mohit Gupta | Astha Agarwal | Pushpak Bhattacharyya
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Shallow Discourse Parsing with Syntactic and (a Few) Semantic Features
Shubham Mukherjee | Abhishek Tiwari | Mohit Gupta | Anil Kumar Singh
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task