Papers by Shib Sankar Dasgupta
AD3: Attentive Deep Document Dater (D18-1)
Copied to clipboard
| Challenge: | Existing methods to predict creation time of documents are based on time-stamp metadata, but none are available. |
| Approach: | They propose an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. |
| Outcome: | The proposed system outperforms neural and non-neural baselines on multiple real-world datasets. |
Dating Documents using Graph Convolution Networks (P18-1)
Copied to clipboard
| Challenge: | Existing approaches for document dating assume accurate knowledge of document date, but this is not always available for arbitrary documents from the Web. |
| Approach: | They propose a Graph Convolutional Network (GCN) based document dating approach which exploits syntactic and temporal graph structures of document in a principled way. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets by 19% absolute accuracy points. |
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (D18-1)
Copied to clipboard
| Challenge: | Existing KG embedding methods ignore this temporal dimension while learning embedds of the KG elements. |
| Approach: | They propose a temporally aware KG embedding method which incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. |
| Outcome: | The proposed method performs KG inference using temporal guidance and predicts scopes for relational facts with missing time annotations. |
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to generalize knowledge bases model triple-level uncertainty . Existing models only model triple level uncertainty, and reasoning results lack global consistency. |
| Approach: | They propose a method to embed knowledge graphs with calibrated probabilistic semantics . they model each entity as a box and relations between two entities as affine transforms based on affinity transforms. |
| Outcome: | Experiments show that the proposed method outperforms baseline methods on confidence prediction and fact ranking. |
Box Embeddings: An open-source library for representation learning using geometric structures (2021.emnlp-demo)
Copied to clipboard
Tejas Chheda, Purujit Goyal, Trang Tran, Dhruvesh Patel, Michael Boratko, Shib Sankar Dasgupta, Andrew McCallum
| Challenge: | Recent studies have explored alternative vector representations with different inductive biases or capabilities. |
| Approach: | They propose a Python library that extends probabilistic box embeddings to geometric shapes and regions. |
| Outcome: | The proposed library is fully open source and compatible with PyTorch and TensorFlow. |