Papers by Mounika Marreddy
How does the brain process syntactic structure while listening? (2023.findings-acl)
Copied to clipboard
| Challenge: | Syntactic parsing is the task of assigning a syntactical structure to a sentence. |
| Approach: | They investigate the predictive power of the brain encoding models in three settings: individual performance of constituency and dependency parsing based embedding methods, relative effectiveness of each of the syntactic parsers, and relative importance of syntaktic information versus semantic information using BERT embeddngs. |
| Outcome: | The proposed methods explain variance in brain regions, especially in listening tasks, and the relative predictive power of the various syntax parsers is unexplored. |
On Robustness of Finetuned Transformer-based NLP Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Pretrained Transformer-based language models have been finetuned for a large number of tasks. |
| Approach: | They characterize changes between pretrained and finetuned models with CKA and STIR metrics. |
| Outcome: | The proposed models are more robust to perturbations than BERT and T5 on classification tasks and generation tasks. |
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity? (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing literature has focused on pretrainer-based text-driven brain encoding models . however, few studies have explored the efficacy of task-specific learning of Transformers . |
| Approach: | They propose to use ten popular natural language processing tasks to learn Transformer representations for predicting brain responses. |
| Outcome: | The proposed model predicts brain activity across the whole brain. |
TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers (2022.acl-srw)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources. |
| Approach: | They propose to use two annotated NER datasets for the Telugu language . they compare the finetuned Telugus model with the existing model in NER . |
| Outcome: | The proposed models outperform existing models on a large dataset of 38,363 sentences on telugu and other languages. |
USDC: A Dataset of ̲User ̲Stance and ̲Dogmatism in Long ̲Conversations (2025.findings-acl)
Copied to clipboard
| Challenge: | Previously, studies on stance and dogmatism in user conversations have focused on training models using annotated datasets at the post level, treating each post as independent and randomly sampling posts from conversation threads. |
| Approach: | They build a dataset for studying user opinion fluctuations in 764 long multi-user Reddit conversation threads, called USDC. |
| Outcome: | The proposed dataset analyzes user opinion fluctuations in 764 long multi-user Reddit conversation threads. |