Papers by Avinash Madasu
Sequential Learning of Convolutional Features for Effective Text Classification (D19-1)
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| Challenge: | Existing models for text classification have largely ignored convolution filters and max pooling . text classification is one of the major applications of natural language processing . |
| Approach: | They propose a convolutional attentive recurrent network model which uses convolution filters and max pooling to improve text classification. |
| Outcome: | The proposed model outperforms existing convolutional models on text classification tasks. |
What do Large Language Models Learn beyond Language? (2022.findings-emnlp)
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| Challenge: | Pretraining on text confers models with useful ‘inductive biases’ for non-linguistic reasoning. |
| Approach: | They investigate whether pre-training on text confers these models with helpful ‘inductive biases’ for non-linguistic reasoning. |
| Outcome: | The proposed models outperform non-pretrained models on 19 non-linguistic tasks and show that they retain inductive biases even when training on multi-lingual text and computer code. |
A Unified Framework for Emotion Identification and Generation in Dialogues (2023.eacl-srw)
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| Challenge: | Social chatbots have gained immense popularity and can be used to develop and promote social chatbot applications. |
| Approach: | They propose a multi-task framework that jointly identifies the emotion of a given dialogue and generates response in accordance to the identified emotion. |
| Outcome: | The proposed framework outperforms current state-of-the-art models with classification and generation loss. |
Pruning the Paradox: How CLIP’s Most Informative Heads Enhance Performance While Amplifying Bias (2025.emnlp-main)
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| Challenge: | Large-scale vision-language models such as CLIP have advanced state-of-the-art performance in vision tasks . however, as they gain prominence in real-world applications, their embedded social biases can be harmful . et al., 2021: 103-104. |
| Approach: | They propose an interpretability metric that measures how consistently attention heads align with specific concepts in CLIP-like models. |
| Outcome: | The proposed interpretability metric measures how consistently attention heads align with specific concepts. |