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Found 7 papers, 3 papers with code
TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers
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.
On Robustness of Finetuned Transformer-based NLP Models
Further, how robust are these models to perturbations in input text?
Syntactic Structure Processing in the Brain while Listening
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.
GAE-ISumm: Unsupervised Graph-Based Summarization of Indian Languages
Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISumm on other Indian languages.
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?
Several popular Transformer based language models have been found to be successful for text-driven brain encoding.
Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language
Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc.
Affect in Tweets Using Experts Model
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).
