Papers by Kashyap Popat
Multilingual Autoregressive Entity Linking (2022.tacl-1)
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Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni
| Challenge: | mGENRE is a sequence-to-sequence system for multilingual entity linking . mGenRE is used to solve language-specific mentions to a multilingual Knowledge Base . |
| Approach: | They propose a sequence-to-sequence system for multilingual entity linking . they match language-specific mentions against a multilingual Knowledge Base (KB) mGENRE is a sequential system that predicts the name of the target entity token-by-token . |
| Outcome: | The proposed system improves on three popular MEL benchmarks and shows improvements in accuracy. |
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (D18-1)
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| Challenge: | Recent work on automated fact-checking does not consider external evidence, but requires rich lexicons. |
| Approach: | They propose a neural network model that aggregates external evidence and language . they also derive informative features for generating user-comprehensible explanations . |
| Outcome: | The proposed model aggregates signals from external evidence articles, language and trustworthiness of their sources without human intervention. |
Open Vocabulary Extreme Classification Using Generative Models (2022.findings-acl)
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Daniel Simig, Fabio Petroni, Pouya Yanki, Kashyap Popat, Christina Du, Sebastian Riedel, Majid Yazdani
| Challenge: | Extreme multi-label classification (XMC) aims at tagging content with subset of labels from an extremely large label set. |
| Approach: | They propose a model that predicts a set of labels outside of the known vocabulary by using a loss-dependent loss-based loss-free model. |
| Outcome: | The proposed model can predict labels outside the known vocabulary while performing on par with state-of-the-art solutions for known labels. |
STANCY: Stance Classification Based on Consistency Cues (D19-1)
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| Challenge: | Recent work has shown that stance classification is a critical step for information credibility and automated fact-checking. |
| Approach: | They propose a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. |
| Outcome: | The proposed model outperforms existing methods on a Perspectrum dataset and shows that it is more accurate than existing methods. |