Papers by Sharmila Reddy Nangi
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer (2021.acl-short)
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| Challenge: | Existing methods for unsupervised text style transfer focus on transferring a specific attribute, but this technique has never been explored in natural language generation tasks. |
| Approach: | They propose a counterfactual-based method to modify latent representations by posing a ‘what-if’ scenario. |
| Outcome: | The proposed method is tested on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support the hypothesis. |
AUTOSUMM: Automatic Model Creation for Text Summarization (2021.emnlp-main)
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Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella
| Challenge: | Recent efforts to develop deep learning models for text generation tasks are challenging for non-experts. |
| Approach: | They propose methods to automatically create deep learning models for extractive and abstractive summarization tasks using large language models. |
| Outcome: | The proposed methods achieve near state-of-the-art performance on a range of datasets. |
Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models (2022.emnlp-main)
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| Challenge: | Existing methods to perform named entity recognition (NER) on unlabeled data are difficult to obtain in low-resource domains. |
| Approach: | They propose ways to use unlabeled data for pretraining to improve performance in downstream tasks. |
| Outcome: | The proposed methods outperform models trained on unlabeled data on seven domains. |