Papers by Chitrank Gupta
TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs (2023.eacl-main)
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| Challenge: | Recent years have witnessed interest in Temporal Question Answering over Knowledge Graphs (TKGQA) but these methods are highly engineered and do not automatically discover relevant parts of the KG during multi-hop reasoning. |
| Approach: | They propose a scheme to modulate the messages passed through a KG edge during convolution based on the relevance of its associated period to the question. |
| Outcome: | The proposed system outperforms state-of-the-art models on a recent challenging dataset for multi-hop complex temporal QA called TimeQuestions. |
ProtoTEx: Explaining Model Decisions with Prototype Tensors (2022.acl-long)
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| Challenge: | Neural models for NLP have yielded significant gains in predictive accuracy across tasks. |
| Approach: | They propose a white-box NLP classification architecture based on prototype networks . they propose an interleaved training algorithm that faithfully explains model decisions . |
| Outcome: | The proposed model matches BART-large and exceeds BERTlarge on propaganda detection tasks. |