no code implementations • COLING 2022 • Subba Reddy Oota, Jashn Arora, Manish Gupta, Raju S. Bapi
(2) Our extensive analysis across 9 broad regions, 11 language sub-regions and 16 visual sub-regions of the brain help us localize, for the first time, the parts of the brain involved in cross-view tasks like image captioning, image tagging, sentence formation and keyword extraction.
no code implementations • ACL 2022 • Suma Reddy Duggenpudi, Subba Reddy Oota, Mounika Marreddy, Radhika Mamidi
Our contributions in this paper include (i) Two annotated NER datasets for the Telugu language in multiple domains: Newswire Dataset (ND) and Medical Dataset (MD), and we combined ND and MD to form Combined Dataset (CD) (ii) Comparison of the finetuned Telugu pretrained transformer models (BERT-Te, RoBERTa-Te, and ELECTRA-Te) with other baseline models (CRF, LSTM-CRF, and BiLSTM-CRF) (iii) Further investigation of the performance of Telugu pretrained transformer models against the multilingual models mBERT, XLM-R, and IndicBERT.
no code implementations • 8 Nov 2023 • Subba Reddy Oota, Emin Çelik, Fatma Deniz, Mariya Toneva
We investigate this question via a direct approach, in which we eliminate information related to specific low-level stimulus features (textual, speech, and visual) in the language model representations, and observe how this intervention affects the alignment with fMRI brain recordings acquired while participants read versus listened to the same naturalistic stories.
no code implementations • 17 Jul 2023 • Subba Reddy Oota, Manish Gupta, Raju S. Bapi, Gael Jobard, Frederic Alexandre, Xavier Hinaut
In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets.
1 code implementation • 23 May 2023 • Pavan Kalyan Reddy Neerudu, Subba Reddy Oota, Mounika Marreddy, Venkateswara Rao Kagita, Manish Gupta
Further, how robust are these models to perturbations in input text?
no code implementations • 16 Feb 2023 • Subba Reddy Oota, Mounika Marreddy, Manish Gupta, Bapi Raju Surampud
In this study, we investigate the predictive power of the brain encoding models in three settings: (i) individual performance of the constituency and dependency syntactic parsing based embedding methods, (ii) efficacy of these syntactic parsing based embedding methods when controlling for basic syntactic signals, (iii) relative effectiveness of each of the syntactic embedding methods when controlling for the other.
1 code implementation • 25 Dec 2022 • Lakshmi Sireesha Vakada, Anudeep Ch, Mounika Marreddy, Subba Reddy Oota, Radhika Mamidi
Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISumm on other Indian languages.
no code implementations • NAACL 2022 • Subba Reddy Oota, Jashn Arora, Veeral Agarwal, Mounika Marreddy, Manish Gupta, Bapi Raju Surampudi
Several popular Transformer based language models have been found to be successful for text-driven brain encoding.
1 code implementation • 2 May 2022 • Mounika Marreddy, Subba Reddy Oota, Lakshmi Sireesha Vakada, Venkata Charan Chinni, Radhika Mamidi
Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc.
no code implementations • COLING 2022 • Subba Reddy Oota, Jashn Arora, Vijay Rowtula, Manish Gupta, Raju S. Bapi
In this paper, we systematically explore the efficacy of image Transformers (ViT, DEiT, and BEiT) and multi-modal Transformers (VisualBERT, LXMERT, and CLIP) for brain encoding.
no code implementations • 18 Apr 2022 • Subba Reddy Oota, Jashn Arora, Manish Gupta, Raju S. Bapi
Also, the decoded representations are sufficiently detailed to enable high accuracy for cross-view-translation tasks with following pairwise accuracy: IC (78. 0), IT (83. 0), KE (83. 7) and SF (74. 5).
no code implementations • 5 Oct 2020 • Subba Reddy Oota, Nafisur Rahman, Shahid Saleem Mohammed, Jeffrey Galitz, Ming Liu
On a combined wound & episode-level data set of patient's wound care information, our extended autoprognosis achieves a recall of 92 and a precision of 92 for the predicting a patient's re-admission risk.
no code implementations • 26 Sep 2019 • Subba Reddy Oota, Naresh Manwani, Raju S. Bapi
In this paper, we achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model.
no code implementations • PACLIC 2018 • Subba Reddy Oota, Adithya Avvaru, Mounika Marreddy, Radhika Mamidi
We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT).
no code implementations • 26 Nov 2018 • Subba Reddy Oota, Adithya Avvaru, Naresh Manwani, Raju S. Bapi
We argue that each expert learns a certain region of brain activations corresponding to its category of words, which solves the problem of identifying the regions with a simple encoding model.
no code implementations • 13 Jun 2018 • Subba Reddy Oota, Naresh Manwani, Bapi Raju S
Unlike the models with hand-crafted features that have been used in the literature, in this paper we propose a novel approach wherein decoding models are built with features extracted from popular linguistic encodings of Word2Vec, GloVe, Meta-Embeddings in conjunction with the empirical fMRI data associated with viewing several dozen concrete nouns.
no code implementations • 8 Oct 2017 • Vijayasaradhi Indurthi, Subba Reddy Oota
Clickbait is a pejorative term describing web content that is aimed at generating online advertising revenue, especially at the expense of quality or accuracy, relying on sensationalist headlines or eye-catching thumbnail pictures to attract click-throughs and to encourage forwarding of the material over online social networks.
no code implementations • SEMEVAL 2017 • Vijayasaradhi Indurthi, Subba Reddy Oota
This paper describes our system for detection and interpretation of English puns.