Papers by Srijan Bansal
Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection (2020.acl-main)
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| Challenge: | In this paper, we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. |
| Approach: | They propose to use code-switching patterns to improve various downstream NLP applications. |
| Outcome: | The proposed features can improve humour, sarcasm and hate speech detection tasks. |
Few-shot Unified Question Answering: Tuning Models or Prompts? (2023.findings-emnlp)
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| Challenge: | Question-answering (QA) tasks investigate specific question types, knowledge domains, or reasoning skills, leading to specialized models catering to specific categories of QA tasks. |
| Approach: | They propose to use model and prompt tuning for unified QA in a low-resource setting to overcome drawbacks of unified models. |
| Outcome: | The proposed model and prompt tuning paradigms outperform model tuning in a few-shot setting with a good initialization and achieve a significant performance boost from pre-training in 'low-resource' setting. |
PRO-CS : An Instance-Based Prompt Composition Technique for Code-Switched Tasks (2022.emnlp-main)
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| Challenge: | Code-switching (CS) data is ubiquitous in today’s globalized world, but the dearth of annotated datasets in code-switch tasks poses a significant challenge for transfer learning in limited-resource setups. |
| Approach: | They propose a prompt composition technique that outperforms prompt-tuning and fine-tuned prompt-based prompt composition techniques for CS tasks that combine language and task knowledge. |
| Outcome: | The proposed approach outperforms prompt-tuning and fine-tuned approaches on 10 datasets across 4 languages and achieves competitive results in low-resource cross-lingual and cross-task setting. |
PEFTDebias : Capturing debiasing information using PEFTs (2023.emnlp-main)
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| Challenge: | Recent research suggests that biases acquired during pretraining can propagate to downstream models, resulting in superficial text dependencies and potential implicit bias. |
| Approach: | They propose a parameter-efficient fine-tuning approach to mitigate implicit biases within foundation models by incorporating parameters into the model and freezing them during the fine-uning process. |
| Outcome: | The proposed method reduces biases in foundation models by incorporating parameters and freezing them during fine-tuning. |
A deep-learning framework to detect sarcasm targets (D19-1)
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| Challenge: | Existing methods for sarcasm target detection are difficult to implement in natural language processing. |
| Approach: | They propose a deep learning framework for sarcasm target detection in predefined sarkastic texts. |
| Outcome: | The proposed framework improves accuracy and accuracy in match and dice scores compared to the current state-of-the-art framework. |
Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings (2025.acl-long)
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| Challenge: | Contextual evaluation is challenging for state-of-the-art judge models . evaluation criteria are often conditional and dependent on practitioner priorities . |
| Approach: | They propose a judge benchmark that evaluates large language models as judges in contexts . they use human annotations and model-based perturbations to build the benchmark . |
| Outcome: | The proposed benchmark aims to evaluate large language models in contexts with 2,000 challenging response pairs. |