Papers by Ayush Maheshwari
ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification (2025.findings-naacl)
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| Challenge: | Existing frameworks for large language models (LLMs) generate high-quality synthetic data that can be used to supplement training data or surpass crowd-sourced annotations. |
| Approach: | They propose a framework that iteratively induces rules and generates synthetic data for text classification. |
| Outcome: | The proposed framework outperforms existing models on in-context learning and fine-tuning settings by using augmented data. |
DictDis: Dictionary Constrained Disambiguation for Improved NMT (2024.findings-emnlp)
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| Challenge: | Existing approaches to domain-specific neural machine translation (NMT) are lexically constrained and draw from domain- specific dictionaries. |
| Approach: | They propose a lexically constrained neural machine translation system that disambiguates between multiple dictionary candidates. |
| Outcome: | The proposed system disambiguates between multiple candidate translations derived from dictionaries on English-Hindi, English-German, and English-French datasets. |
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming (2022.findings-acl)
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Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa
| Challenge: | supervised machine learning requires large amounts of labeled data to train models. |
| Approach: | They propose a framework to generate human-interpretable labeling functions . they propose to learn a model on the same labeled dataset and unlabeled data . |
| Outcome: | The proposed framework outperforms prior approaches on several text classification datasets. |
Semi-Supervised Data Programming with Subset Selection (2021.findings-acl)
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| Challenge: | Several approaches to improve annotation cost have been proposed to use large amounts of labelled training data. |
| Approach: | They propose a semi-supervised data programming paradigm that uses weak supervision and semi-supervised loss functions to augment small amounts of labelled data with a large unlabelled dataset. |
| Outcome: | The proposed framework outperforms the current state-of-the-art on seven publicly available datasets. |
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification (2021.eacl-main)
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| Challenge: | Existing methods for hierarchical multi-label classification do not assume label hierarchy exists. |
| Approach: | They propose to jointly learn the classifier parameters as well as the label embeddings . they propose to use hyperbolic embeddables to gain better generalisation over the labels . |
| Outcome: | The proposed method achieves state-of-the-art generalization on benchmarks and is more accurate than existing methods. |
Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data (N18-5)
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| Challenge: | Existing systems for resolving entities and disambiguating locations based on publicly available web data are challenging because of the limited information available on the Web. |
| Approach: | They propose a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples. |
| Outcome: | The proposed system resolves entities and disambiguates locations with high confidence using grammar rules and clustering algorithms. |
A Benchmark and Dataset for Post-OCR text correction in Sanskrit (2022.findings-emnlp)
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| Challenge: | Sanskrit is a classical language with 30 million manuscripts available for digitisation . however, it is considered to be low-resource when it comes to available digital resources. |
| Approach: | They propose to use a post-OCR text correction dataset to correct errors from OCR predictions from 30 different books in the Indian subcontinent. |
| Outcome: | The proposed model outperforms OCR models on graphemic and lexical levels and shows that it is more accurate than previous models. |
Samayik: A Benchmark and Dataset for English-Sanskrit Translation (2024.lrec-main)
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Ayush Maheshwari, Ashim Gupta, Amrith Krishna, Atul Kumar Singh, Ganesh Ramakrishnan, Anil Kumar Gourishetty, Jitin Singla
| Challenge: | Existing Sanskrit corpora focus on poetry and offer limited coverage of contemporary written materials. |
| Approach: | They release a dataset of 53,000 parallel English-Sanskrit sentences . they use spoken content that covers contemporary world affairs and interpretations . |
| Outcome: | a new dataset of 53,000 parallel English-Sanskrit sentences is released . the dataset outperforms existing models trained on older classical-era poetry datasets . |
Rule Augmented Unsupervised Constituency Parsing (2021.findings-acl)
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| Challenge: | Recent studies have shown that unsupervised parsing methods do not learn meaningful semantics (not even simple grammar) |
| Approach: | They propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic grammar rules and is independent of the base system. |
| Outcome: | The proposed model is independent of the base system and takes advantage of syntactic grammar rules. |
LexGen: Domain-aware Multilingual Lexicon Generation (2025.acl-long)
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Ayush Maheshwari, Atul Kumar Singh, N J Karthika, Krishnakant Bhatt, Preethi Jyothi, Ganesh Ramakrishnan
| Challenge: | Lexicon generation is a key task in specialized domains due to infrequent usage of terms . a new model is proposed to generate dictionary words for 6 Indian languages . |
| Approach: | They propose a model to generate dictionary words for 6 Indian languages in the multi-domain setting. |
| Outcome: | The proposed model generalizes to unseen domains and unsealed languages. |
SPEAR : Semi-supervised Data Programming in Python (2022.emnlp-demos)
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Guttu Abhishek, Harshad Ingole, Parth Laturia, Vineeth Dorna, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh Iyer
| Challenge: | a package for data programming with semi supervision implements several recent approaches to label and train machine learning models. |
| Approach: | They propose an open-source python library for data programming with semi supervision . the library implements several recent data programming approaches including heuristics and association of noisy labels to training datasets. |
| Outcome: | The proposed package implements several recent approaches for data programming with semi supervision. |
FAIR: Filtering of Automatically Induced Rules (2024.eacl-long)
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| Challenge: | Existing methods to reduce the human annotation efforts require a diverse set of rules to assign labels to unlabeled data. |
| Approach: | They propose an automatic rule-filtering algorithm to filter out a large set of automatically created rules from a small set of labeled features. |
| Outcome: | The proposed approach achieves statistically significant results over existing methods. |