Papers by Sandeep Suntwal
Data and Model Distillation as a Solution for Domain-transferable Fact Verification (2021.naacl-main)
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| Challenge: | Neural networks depend heavily on lexicalized information, which transfers poorly between domains. |
| Approach: | They propose a method to delexicize lexicalized data and a model distillation technique to prevent aggressive data distillation. |
| Outcome: | The proposed method improves performance on lexicalized data and out of domain models. |
Towards the Necessity for Debiasing Natural Language Inference Datasets (2020.lrec-1)
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| Challenge: | Delexicalization of datasets can improve natural language inference performance . a dataset with a delexicalized version of the FEVER dataset is used for natural language learning . |
| Approach: | They propose two techniques for delexicalization that modify annotated datasets to control the importance of lexical entities. |
| Outcome: | The proposed methods maintain performance in-domain and improve performance in some out-of-domain settings. |
On the Importance of Delexicalization for Fact Verification (D19-1)
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| Challenge: | Neural networks (NNs) perform state-of-the-art (SOA) performance in many complex tasks. |
| Approach: | They investigate the importance that a model assigns to various aspects of data . they experiment with two strategies of masking to mitigate this dependence on lexicalized information . |
| Outcome: | The proposed model improves on the in-domain dataset by 10% compared to the fully lexicalized model. |
Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification (2021.emnlp-main)
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| Challenge: | Neural networks depend heavily on lexicalized information, which can be overfitted . this can be a problem in fact verification, which has important societal implications. |
| Approach: | They propose a knowledge distillation approach for fact verification using student models. |
| Outcome: | The proposed approach outperforms state-of-the-art classifiers on a training dataset and in supervised settings. |