Papers by Ashutosh Kumar
Syntax-Guided Controlled Generation of Paraphrases (2020.tacl-1)
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| Challenge: | Recent work has explored the incorporation of complex syntactic-guidance as constraints in the task of controlled text generation. |
| Approach: | They propose an end-to-end framework for controlled paraphrase generation that incorporates complex syntactic-guidance constraints into the task. |
| Outcome: | The proposed framework generates syntax-conforming sentences while not compromising on relevance. |
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation (N19-1)
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| Challenge: | Previous work focused on generating semantically similar paraphrases without considering diversity. |
| Approach: | They propose a method to obtain highly diverse paraphrases without compromising on paraphrasing quality by using monotone submodular function maximization. |
| Outcome: | The proposed method is effective on multiple tasks such as intent classification and paraphrase recognition. |
Generative or Discriminative? Revisiting Text Classification in the Era of Transformers (2025.emnlp-main)
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Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury, Ashutosh Kumar, Yaswanth Biruduraju, Santhosh Kumar Kasa, Pattisapu Nikhil Priyatam, Arindam Bhattacharya, Shailendra Agarwal, Vijay Huddar
| Challenge: | generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, a trade-off that remains unexplored in the transformer era. |
| Approach: | They propose to evaluate generative and discriminative architectures for text classification using a generative model that learns the conditional probability distribution P (y|x) generative models are known to work better in low-data settings, giving rise to the classical 'two regimes' phenomenon for classification. |
| Outcome: | The proposed models show that the classical 'two regimes' manifests distinctly across different architectures and training paradigms. |
BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain (2024.naacl-long)
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| Challenge: | Existing models for accounting databases that can be queried using natural language are lacking in some domains. |
| Approach: | They propose a large-scale text-to-SQL dataset for accounting and financial domains . they propose 'bookSQl' to be used to query accounting databases using natural language . |
| Outcome: | The proposed model performs poorly on the existing model, pointing towards a more focused model for this domain. |
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks (2022.findings-acl)
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| Challenge: | Inconsistency is observed in symmetric classification tasks that take two inputs and require the output to be invariant of the order of the inputs. |
| Approach: | They propose a consistency loss function to alleviate inconsistency in symmetric classification tasks that take two inputs and require the output to be invariant of the order of the inputs. |
| Outcome: | The proposed model improves consistency in predictions for three paraphrase detection datasets without significant drop in accuracy scores. |