Papers by Deepak Kumar
Parameter-efficient Modularised Bias Mitigation via AdapterFusion (2023.eacl-main)
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Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz
| Challenge: | Large pre-trained language models contain societal biases and carry along these biase . Current approaches to mitigate these bias impose debiasing by updating model parameters, effectively transferring model to irreversible debiased state. |
| Approach: | They propose to develop stand-alone debiasing functionalities separate from the model, which can be integrated into the model on-demand while keeping the core model untouched. |
| Outcome: | The proposed approach improves or maintains effectiveness of bias mitigation, avoids catastrophic forgetting in a multi-attribute scenario, and maintains on-par task performance while granting parameter-efficiency and easy switching between the original and debiased models. |
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. |
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages (2022.tacl-1)
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Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj, Mayank Jobanputra, Raghavan AK, Ajitesh Sharma, Sujit Sahoo, Harshita Diddee, Mahalakshmi J, Divyanshu Kakwani, Navneet Kumar, Aswin Pradeep, Srihari Nagaraj, Kumar Deepak, Vivek Raghavan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh Shantadevi Khapra
| Challenge: | We present Samanantar, the largest publicly available parallel corpora collection for Indic languages . based on existing corporative, there has been limited benefit for resource-poor languages despite the lack of parallel corporals and monolingual corporata. |
| Approach: | They compile 12.4 million sentence pairs from existing corpora and mine 37.4 million from the Web. |
| Outcome: | The proposed model outperforms existing models and benchmarks on public datasets. |
Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs (2026.acl-long)
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| Challenge: | Spontaneous speech is rarely fluent, and disfluencies can degrade readability and reliability . a sequence tagger first marks disfluent tokens, and these signals guide instruction fine-tuning . |
| Approach: | They propose a multilingual correction pipeline where a sequence tagger first marks disfluent tokens . they add a contrastive learning objective that penalizes the reproduction of disfluency tokens. |
| Outcome: | The proposed model improves readability and reliability of ASR transcripts in three languages . disfluencies can cause misinterpretations, incoherent responses, poor user experience . |
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks (2023.findings-acl)
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| Challenge: | Existing studies show that pre-trained language models can be used to mitigate societal biases and stereotypes. |
| Approach: | They propose a modular bias mitigation approach that integrates debiasing modules into the core model on-demand at inference time. |
| Outcome: | The proposed approach improves on-par with baseline finetuning on gender, race, and age protected attributes on three classification tasks with gender, age, and race as protected attributes. |