Papers by Jatin Chauhan
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
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| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
| Approach: | They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model. |
| Outcome: | The proposed model captures both entity and relation features in any given neighborhood and also encapsulates relation clusters and multi-hop relations. |
Learning under Label Proportions for Text Classification (2023.findings-emnlp)
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| Challenge: | Using a set of data samples with ground truth labels, learning from label proportions is infeasible in many cases. |
| Approach: | They propose a setup where the data is provided in an aggregate form called bags and only the proportion of samples belonging to each class as the ground truth. |
| Outcome: | The proposed model achieves better results than baselines in almost 87% of the configurations. |