Papers by Subba Reddy Oota
How does the brain process syntactic structure while listening? (2023.findings-acl)
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| 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. |
Aligning Text/Speech Representations from Multimodal Models with MEG Brain Activity During Listening (2025.emnlp-main)
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Padakanti Srijith, Khushbu Pahwa, Radhika Mamidi, Bapi Raju Surampudi, Manish Gupta, Subba Reddy Oota
| Challenge: | Recent studies have found that speech language models fail to capture brain-relevant semantics beyond low-level features. |
| Approach: | They analyze multimodal models to assess their alignment with MEG brain recordings . they find text embeddings from multimodal and unimodal models significantly outperform unilateral models . |
| Outcome: | a new study shows that text-based models outperform unimodal models in alignment with brain recordings during naturalistic story listening. |
Speech language models lack important brain-relevant semantics (2024.acl-long)
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| Challenge: | Recent work shows that text-based language models predict both text- and speech-evoked brain activity. |
| Approach: | They remove low-level stimulus features from language models to assess their impact on alignment with fMRI brain recordings during reading and listening. |
| Outcome: | The proposed model removes low-level features from fMRI brain recordings to assess their impact on alignment with fmr recordings. |
Visio-Linguistic Brain Encoding (2022.coling-1)
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| Challenge: | Existing studies have failed to explore co-attentive multi-modal modeling for visual and text reasoning. |
| Approach: | They propose to use image and multi-modal Transformers to reconstruct fMRI brain activity . they use two popular datasets to study visual and text reasoning . |
| Outcome: | The proposed model outperforms existing models on two popular datasets . the results raise the question whether visual processing is affected implicitly by linguistic processing . |
Multi-view and Cross-view Brain Decoding (2022.coling-1)
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| Challenge: | a recent study has shown that brain decoding models can decode concepts from single view . a multi-view decoder can take brain recordings for any view as input and predict the concept . |
| Approach: | They propose to build a multi-view decoder that can take brain recordings for any view as input and predict the concept. |
| Outcome: | The proposed systems can decode concepts from brain recordings from any view . the proposed systems have 0.68 pairwise accuracy across view pairs and 0.8 average pairwise precision across tasks. |
Encoding and Decoding Language in the Brain with Language Models (2026.eacl-tutorials)
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| Challenge: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and brain-based fine-caching with language models. |
| Approach: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and scaling with language models. |
| Outcome: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and decoding with language models. |
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity? (2022.naacl-main)
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| 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)
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| 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)
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| 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. |