Rajat Patel


2023

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InterosML@Causal News Corpus 2023: Understanding Causal Relationships: Supervised Contrastive Learning for Event Classification
Rajat Patel
Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text

Causal events play a crucial role in explaining the intricate relationships between the causes and effects of events. However, comprehending causal events within discourse, text, or speech poses significant semantic challenges. We propose a contrastive learning-based method in this submission to the Causal News Corpus - Event Causality Shared Task 2023, with a specific focus on SubTask1 centered on causal event classification. In our approach we pre-train our base model using Supervised Contrastive (SuperCon) learning. Subsequently, we fine-tune the pre-trained model for the specific task of causal event classification. Our experimentation demonstrates the effectiveness of our method, achieving a competitive performance, and securing the 2nd position on the leaderboard with an F1-Score of 84.36.

2020

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On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling
Rajat Patel | Francis Ferraro
Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures

We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work also shows that jointly modeling both structured knowledge tuples and language improves both.