Papers by Abhishek Singh
Incorporating Emoji Descriptions Improves Tweet Classification (N19-1)
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| Challenge: | Tweets are short messages that often include specialized language such as hashtags and emojis. |
| Approach: | They propose a simple strategy to replace emojis with their natural language description and use pretrained word embeddings to process tweets. |
| Outcome: | The proposed method is more effective than pretrained emoji embeddings for tweet classification. |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| Challenge: | Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph . |
| Approach: | They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG . |
| Outcome: | The proposed method is more accurate than state-of-the-art methods on standard datasets. |
Generalizable and Stable Finetuning of Pretrained Language Models on Low-Resource Texts (2024.naacl-long)
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| Challenge: | Pretrained language models have advanced natural language processing tasks significantly, but finetuning them on low-resource datasets presents significant challenges such as instability and overfitting. |
| Approach: | They propose a regularization method based on attention-guided weight mixup for finetuning PLMs on low-resource datasets. |
| Outcome: | The proposed method improves generalization and combats overfitting on two splits of the training dataset. |