Papers by Kaushal Maurya
DivHSK: Diverse Headline Generation using Self-Attention based Keyword Selection (2023.findings-acl)
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| Challenge: | Diverse headline generation is an NLP task where the goal is to generate multiple headlines that are true to the content of the article but are different among themselves. |
| Approach: | They propose a novel model that generates multiple diverse headlines by using a pre-trained encoder and a cluster-specific keyword set. |
| Outcome: | The proposed model outperforms existing literature and their strong baselines and emerges as a state-of-the-art model. |
CharSpan: Utilizing Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages (2024.eacl-short)
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| Challenge: | Existing models for ELRLs lack parallel corpora and monolingual corporata . authors propose novel character-span noise argumentation model to facilitate cross-lingual transfer . |
| Approach: | They propose a character-span noise argumentation model to facilitate cross-lingual transfer . they use character-size noise argumentations to regularize training data of HRL . |
| Outcome: | The proposed model outperforms baselines on closely related HRL-ELRL pairs from three different language families. |
SelectNoise: Unsupervised Noise Injection to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages (2023.findings-emnlp)
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| Challenge: | Currently, MT systems for low-resource languages lack parallel data and monolingual data. |
| Approach: | They propose an unsupervised approach to generate noisy HRLs training data by selective candidate extraction and noise injection. |
| Outcome: | The proposed model outperforms strong baselines on 12 ELRLs in a zero-shot setting . |
Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation (2022.findings-acl)
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| Challenge: | Existing approaches to learn shareable structures from low-resource languages are limited in the zero-shot setting. |
| Approach: | They propose a meta-learning framework to learn shareable structures from typologically diverse languages based on meta- learning and language clustering. |
| Outcome: | The proposed framework is able to learn shareable structures from typologically diverse languages with limited annotated data. |