Search Results for author: Anmol Agarwal

Found 5 papers, 3 papers with code

Towards Effective Paraphrasing for Information Disguise

1 code implementation8 Nov 2023 Anmol Agarwal, Shrey Gupta, Vamshi Bonagiri, Manas Gaur, Joseph Reagle, Ponnurangam Kumaraguru

Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors' posts on the Internet.

Ethics Sentence

AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs

no code implementations5 Nov 2023 Yann Hicke, Anmol Agarwal, Qianou Ma, Paul Denny

Responding to the thousands of student questions on online QA platforms each semester has a considerable human cost, particularly in computing courses with rapidly growing enrollments.

Question Answering Retrieval

CST5: Data Augmentation for Code-Switched Semantic Parsing

1 code implementation14 Nov 2022 Anmol Agarwal, Jigar Gupta, Rahul Goel, Shyam Upadhyay, Pankaj Joshi, Rengarajan Aravamudhan

To aid further research in this area, we are also releasing (a) Hinglish-TOP, the largest human annotated code-switched semantic parsing dataset to date, containing 10k human annotated Hindi-English (Hinglish) code-switched utterances, and (b) Over 170K CST5 generated code-switched utterances from the TOPv2 dataset.

Data Augmentation Semantic Parsing

Diagnosing Data from ICTs to Provide Focused Assistance in Agricultural Adoptions

no code implementations29 Oct 2021 Ashwin Singh, Mallika Subramanian, Anmol Agarwal, Pratyush Priyadarshi, Shrey Gupta, Kiran Garimella, Sanjeev Kumar, Ritesh Kumar, Lokesh Garg, Erica Arya, Ponnurangam Kumaraguru

Our classifier achieves accuracies ranging from 79% to 90% across the five states, demonstrating its potential for assisting future ethnographic investigations.

Specificity

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