Papers by Shivashankar Subramanian
Fairness-aware Class Imbalanced Learning (2021.emnlp-main)
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| Challenge: | Existing studies on class imbalance and mitigating bias have focused on the latter . a skewed class distribution hurts the performance of deep learning models, and is often referred to as "stereotyping" |
| Approach: | They propose to extend a margin-loss based approach to enforce fairness by using tweet sentiment and occupation classification to mitigate class imbalance and demographic bias. |
| Outcome: | The proposed methods help mitigate class imbalance and demographic biases through controlled experiments. |
Evaluating Debiasing Techniques for Intersectional Biases (2021.emnlp-main)
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| Challenge: | Existing methods for debiasing protected attributes have been limited to binary attributes in isolation, however many corpora involve multiple such attributes, possibly with higher cardinality. |
| Approach: | They propose to evaluate a bias-constrained model which is new to NLP and an extension of the iterative nullspace projection technique which can handle multiple identities. |
| Outcome: | The proposed model is based on a new iterative nullspace projection technique which can handle multiple identities. |
Towards Improved Multi-Source Attribution for Long-Form Answer Generation (2024.naacl-long)
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| Challenge: | Current LLMs struggle with attribution for long-form answers which require reasoning over multiple evidence sources. |
| Approach: | They propose to improve attribution capability of large language models for long-form answer generation to multiple sources with multiple citations per sentence. |
| Outcome: | The proposed model improves on a wide range of attribution benchmark datasets on PolitiICite, a multi-source attribution dataset based on PolitIcite articles . |
Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis (N18-1)
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| Challenge: | Election manifestos document the intentions, motives, and views of political parties. |
| Approach: | They propose a hierarchical structured deep model to predict fine- and coarse-grained positions and a probabilistic soft logic model to perform post-hoc calibration of coarse- and fine-grain positions. |
| Outcome: | The proposed model outperforms state-of-the-art approaches at both granularities using manifestos from twelve countries, written in ten different languages. |
Deep Ordinal Regression for Pledge Specificity Prediction (D19-1)
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| Challenge: | Currently, there are no publicly available annotated datasets of pledges . a novel approach to specificity prediction is needed to predict the specificity of pledged issues. |
| Approach: | They propose deep ordinal regression approaches for specificity prediction using supervised and semi-supervised settings. |
| Outcome: | The proposed methods demonstrate their utility over several baseline approaches. |
Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model (P18-2)
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| Challenge: | Existing work on predicting popularity of online petitions based on initial popularity trajectory has focused on estimating the number of signatures a petition gets in the first x hours, and predicting the total number of signed petitions at the end of its lifetime. |
| Approach: | They propose a CNN-based model to predict the popularity of a petition based on its textual content and use it to model the influence of other petition signers. |
| Outcome: | The proposed model is based on UK and US government petition datasets and is compared with previous work on predicting popularity over time based upon initial popularity trajectory. |
APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI (2026.acl-long)
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| Challenge: | Large language models struggle with reliable long-term conversational memory . enlarging context windows or applying nave retrieval often introduces noise . |
| Approach: | They propose a conversational memory system that uses domain-agnostic ontology to structure conversations as temporally grounded events in an entity-centric framework. |
| Outcome: | APEX-MEM outperforms state-of-the-art retrieval methods in accuracy and time resolution. |