Shubham Gupta


2024

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Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
Lokesh Mishra | Sohayl Dhibi | Yusik Kim | Cesar Berrospi Ramis | Shubham Gupta | Michele Dolfi | Peter Staar
Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)

Environment, Social, and Governance (ESG) KPIs assess an organization’s performance on issues such as climate change, greenhouse gas emissions, water consumption, waste management, human rights, diversity, and policies. ESG reports convey this valuable quantitative information through tables. Unfortunately, extracting this information is difficult due to high variability in the table structure as well as content. We propose Statements, a novel domain agnostic data structure for extracting quantitative facts and related information. We propose translating tables to statements as a new supervised deep-learning universal information extraction task. We introduce SemTabNet - a dataset of over 100K annotated tables. Investigating a family of T5-based Statement Extraction Models, our best model generates statements which are 82% similar to the ground-truth (compared to baseline of 21%). We demonstrate the advantages of statements by applying our model to over 2700 tables from ESG reports. The homogeneous nature of statements permits exploratory data analysis on expansive information found in large collections of ESG reports.

2022

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Incorporating Linguistic Knowledge for Abstractive Multi-document Summarization
Congbo Ma | Wei Emma Zhang | Hu Wang | Shubham Gupta | Mingyu Guo
Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation

2021

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Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
Rishi Hazra | Parag Dutta | Shubham Gupta | Mohammed Abdul Qaathir | Ambedkar Dukkipati
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model. In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process. Our proposed approach, Active2 Learning (A2L), actively adapts to the deep learning model being trained to eliminate such redundant examples chosen by an AL strategy. We show that A2L is widely applicable by using it in conjunction with several different AL strategies and NLP tasks. We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by 3-25% on an absolute scale on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead.