Papers by Samarth Bhargav
Towards Reproducible Machine Learning Research in Natural Language Processing (2022.acl-tutorials)
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Ana Lucic, Maurits Bleeker, Samarth Bhargav, Jessica Forde, Koustuv Sinha, Jesse Dodge, Sasha Luccioni, Robert Stojnic
| Challenge: | a tutorial on reproducibility in ML addresses the problem of research results that are not reproducible. |
| Approach: | They propose a tutorial to ensure reproducible research in ML with an emphasis on computational linguistics and NLP. |
| Outcome: | The proposed tutorial focuses on computational linguistics and NLP . it provides a framework for using reproducibility as a teaching tool in university-level computer science programs. |
A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers (2025.findings-emnlp)
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| Challenge: | a new taxonomy of negation is proposed to improve neural information retrieval models . negation types are covered in existing datasets, allowing for faster convergence . |
| Approach: | They propose a taxonomy of negation that derives from philosophical, linguistic, and logical definitions . they also propose analyzing the performance of retrieval models on existing datasets using a logic-based classification mechanism. |
| Outcome: | The proposed taxonomy produces a balanced data distribution over negation types . it also provides a better training setup that leads to faster convergence on the NevIR dataset . |
Robustness Evaluation of Entity Disambiguation Using Prior Probes: the Case of Entity Overshadowing (2021.emnlp-main)
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| Challenge: | Entity disambiguation (ED) is the last step of entity linking when candidate entities are reranked according to the context they appear in. |
| Approach: | They propose a dataset that includes 16K short text snippets annotated with entity mentions to evaluate EL models. |
| Outcome: | The proposed dataset shows that the performance of EL systems is overestimated . the results show that the EL system performance is significantly better on the ShadowLink benchmark . |