Papers by Sarthak Dash
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding (2022.findings-naacl)
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| Challenge: | Existing methods for encoding text in tables require additional training and require additional pretraining. |
| Approach: | They propose a novel encoding strategy that preserves the critical property of permutation invariance across rows or columns. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three table interpretation tasks: column type annotation, relation extraction, and entity linking. |
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer (2020.acl-main)
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| Challenge: | Existing taxonomies are either entirely absent or missing. |
| Approach: | They propose a GNN-based cross-domain transfer framework for the taxonomy construction task. |
| Outcome: | The proposed framework improves on benchmark datasets from science and environment domains. |
Automatic Taxonomy Induction and Expansion (D19-3)
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Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Sarthak Dash, Md. Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya
| Challenge: | Knowledge Graph Induction Service (KGIS) enables automatic taxonomy induction and human-in-the-loop curation. |
| Approach: | They describe the features of the Knowledge Graph Induction Service (KGIS) KGIS allows the user to semi-automatically curate and expand the induced taxonomies through a component called Smart SpreadSheet . |
| Outcome: | The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge graph induction system. |
Open Knowledge Graphs Canonicalization using Variational Autoencoders (2021.emnlp-main)
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| Challenge: | Existing approaches to solve this problem generate embeddings for noun and relation phrases . ambiguous subject-relation-object triples are created by open knowledge graphs . |
| Approach: | They propose a model to learn both embeddings and cluster assignments in an end-to-end approach . they propose CUVA to be able to group noun and relation phrases using embeddable features . |
| Outcome: | The proposed model outperforms state-of-the-art methods over multiple benchmarks. |