Dr Nafise Sadat Moosavi
Department of Computer Science
Lecturer in Natural Language Processing
Member of the Natural Language Processing research group
Deputy Director of Equality, Diversity and Inclusion
+44 114 222 1943
Full contact details
Department of Computer Science
Regent Court (DCS)
211 Portobello
Sheffield
S1 4DP
- Profile
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Dr. Nafise Sadat Moosavi is a Lecturer in Natural Language Processing at the Computer Science Department of the University of Sheffield. Before joining the University of Sheffield, she was a postdoctoral researcher at the UKP Lab at the Technical University of Darmstadt. Before that, she got her PhD degree from Heidelberg University. She received her bachelor's and master's degrees in computer science from Alzahra University and the Sharif University of Technology in Tehran, Iran.
- Research interests
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She is interested in various areas of Natural Language Processing and Machine Learning including end-to-end reasoning, robustness and generalization, coreference resolution, text generation, sustainability, and reliable evaluation.
- Publications
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Featured publications
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All publications
Journal articles
- Scoring Coreference Chains with Split-Antecedent Anaphors. Dialogue & Discourse, 14(2), 1-48.
- NL-Augmenter . Northern European Journal of Language Technology, 9(1).
Conference proceedings papers
- Lessons Learned from a Citizen Science Project for Natural Language Processing. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp 3594-3608), 2 May 2023 - 6 May 2023.
- Transformers with Learnable Activation Functions. Findings of the Association for Computational Linguistics: EACL 2023, 2 May 2023 - 6 May 2023.
- FERMAT: An Alternative to Accuracy for Numerical Reasoning. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), July 2023 - July 2023.
- Arithmetic-Based Pretraining Improving Numeracy of Pretrained Language Models. Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023), July 2023 - July 2023.
- Lessons Learned from a Citizen Science Project for Natural Language Processing. EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp 3576-3590)
- Transformers with Learnable Activation Functions. EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023 (pp 2337-2353)
- Layer or representation space: what makes BERT-based evaluation metrics robust?. Proceedings of the 29th International Conference on Computational Linguistics, Vol. 29(1) (pp 3401-3411). Gyeongju, Republic of Korea, 12 October 2022 - 12 October 2022.
- The Universal Anaphora Scorer. 2022 Language Resources and Evaluation Conference, LREC 2022 (pp 4873-4883)
- SciGen: a Dataset for Reasoning-Aware Text Generation from Scientific Tables. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, Vol. 1, 6 December 2021 - 11 December 2021.
- Coreference Reasoning in Machine Reading Comprehension. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), August 2021 - August 2021.
- Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, November 2021 - November 2021.
- Stay Together: A System for Single and Split-antecedent Anaphora Resolution. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2021 - June 2021.
- Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, July 2020 - July 2020.
- Improving QA Generalization by Concurrent Modeling of Multiple Biases. Findings of the Association for Computational Linguistics: EMNLP 2020, November 2020 - November 2020.
- Towards Debiasing NLU Models from Unknown Biases. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), November 2020 - November 2020.
- Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution. Proceedings of the 28th International Conference on Computational Linguistics, December 2020 - December 2020.
- Neural Duplicate Question Detection without Labeled Training Data. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), November 2019 - November 2019.
- Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, July 2019 - July 2019.
- Anaphora Resolution with the ARRAU Corpus. Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference, June 2018 - June 2018.
- Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, October 2018 - November 2018.
- Revisiting Selectional Preferences for Coreference Resolution. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, September 2017 - September 2017.
- Lexical Features in Coreference Resolution: To be Used With Caution. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), July 2017 - July 2017.
- Use Generalized Representations, But Do Not Forget Surface Features. Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017), April 2017 - April 2017.
- Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), August 2016 - August 2016.
- Search Space Pruning: A Simple Solution for Better Coreference Resolvers. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2016 - June 2016.
- Unsupervised Coreference Resolution Using a Graph Labeling Approach. Human Language Technology Challenges for Computer Science and Linguistics (pp 93-103), 25 November 2011 - 27 November 2011.
- Unsupervised coreference resolution by utilizing the most informative relations. COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers (pp 644-655)
- TCvisor: A hypervisor level secure storage. 2010 International Conference for Internet Technology and Secured Transactions, ICITST 2010
- Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization. Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics
- Adaptable Adapters. Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics
- COALA: A Neural Coverage-Based Approach for Long Answer Selection with Small Data. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33 (pp 6932-6939) View this article in WRRO
Preprints
- FERMAT: An Alternative to Accuracy for Numerical Reasoning, arXiv.
- Lessons Learned from a Citizen Science Project for Natural Language Processing, arXiv.
- Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?, arXiv.
- Transformers with Learnable Activation Functions, arXiv.
- Scoring Coreference Chains with Split-Antecedent Anaphors, arXiv.
- Improving the Numerical Reasoning Skills of Pretrained Language Models, arXiv.
- Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization, arXiv.
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation, arXiv.
- Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures, arXiv.
- Improving Generalization by Incorporating Coverage in Natural Language Inference, arXiv.