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.
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 • 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).