---
title: "Who cites Transforming Question — 107 citing papers"
description: "107 papers of the Elamur Research library cite «Transforming Question Answering Datasets Into Natural Language Inference Datasets»…"
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# Who cites «Transforming Question Answering Datasets Into Natural Language Inference Datasets»

107 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · [← back to «Transforming Question Answering Datasets Into Natural Language Inference Datasets»](https://md.research.elamur.ai/free/en/papers/demszky-2018-transforming-question-answering-datasets-natural/)

1. **[BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Christopher Clark, Kenton Lee, Ming-Wei Chang, et al.*2019 · 1,330 citations*
2. **[Dialogue Natural Language Inference](https://md.research.elamur.ai/free/en/papers/welleck-2019-dialogue-natural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sean Welleck, Jason Weston, Arthur Szlam, et al.*2019 · 385 citations*
3. **[FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Esin Durmus, He He, Mona Diab*2020 · 359 citations*
4. **[Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 297 citations*
5. **[QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization](https://md.research.elamur.ai/free/en/papers/fabbri-2022-qafacteval-improved-qa-based-factual/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, et al.*2022 · 292 citations*
6. **[AlignScore: Evaluating Factual Consistency with A Unified Alignment Function](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuheng Zha, Yichi Yang, Ruichen Li, et al.*2023 · 284 citations*
7. **[Explaining Answers with Entailment Trees](https://md.research.elamur.ai/free/en/papers/dalvi-2021-explaining-answers-entailment-trees/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, et al.*2021 · 211 citations*
8. [**DART: Open-Domain Structured Data Record to Text Generation arXiv:2007.02871**Linyong Nan, Dragomir Radev, Rui Zhang, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/nan-2020-dart-open-domain-structured-data/)
9. **[Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary](https://md.research.elamur.ai/free/en/papers/deutsch-2021-question-answering-as-automatic-metric/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniel Deutsch, Tania Bedrax-Weiss, Dan Roth*2021 · 152 citations*
10. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
11. **[Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning](https://md.research.elamur.ai/free/en/papers/tafjord-2022-entailer-answering-questions-faithful/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark*2022 · 71 citations*
12. **[ANLIzing the Adversarial Natural Language Inference Dataset](https://md.research.elamur.ai/free/en/papers/williams-2022-anlizing-adversarial-natural-language-inference/) [Society for Computation in Linguistics (SCiL)](https://md.research.elamur.ai/free/en/venues/scil/)**Adina Williams, Tristan Thrush, Douwe Kiela*2022 · 48 citations*
13. **[Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data](https://md.research.elamur.ai/free/en/papers/akhtar-2023-exploring-numerical-reasoning-capabilities-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, et al.*2023 · 38 citations*
14. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
15. **[Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic](https://md.research.elamur.ai/free/en/papers/weir-2024-enhancing-systematic-decompositional-natural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nathaniel Weir, Kate Sanders, Orion Weller, et al.*2024 · 7 citations*
16. **[Evaluating Numeracy of Language Models as a Natural Language Inference Task](https://md.research.elamur.ai/free/en/papers/mahendra-2025-evaluating-numeracy-language-models-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rahmad Mahendra, Damiano Spina, Lawrence Cavedon, et al.*2025 · 4 citations*
17. **[How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics](https://md.research.elamur.ai/free/en/papers/cosma-2024-how-hard-is-this-test/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Adrian Cosma, Stefan Ruseti, Mihai Dascalu, et al.*2024 · 3 citations*
18. [**Understanding Roles and Entities: Datasets and Models for Natural Language Inference arXiv:1904.09720**Arindam Mitra, Ishan Shrivastava, Chitta Baral*2019 · 2 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2019-understanding-roles-entities-datasets-models/)
19. **[Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions](https://md.research.elamur.ai/free/en/papers/zhang-2026-polynomial-expansion-rank-adaptation-enhancing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenhao Zhang, Lin Mu, Li Ni, et al.*2026 · 1 citation*
20. **[From Multiple-Choice to Extractive QA: A Case Study for English and Arabic](https://md.research.elamur.ai/free/en/papers/lynn-2025-multiple-choice-extractive-qa-case/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Teresa Lynn, Malik H. Altakrori, Samar M. Magdy, et al.*2025 · 1 citation*
21. **[Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/liu-2026-specialization-without-sparsity-efficient-expressive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ganghao Liu, Qin Zhou, Zhe Wang, et al.*2026*
22. **[Ultra-Low-Dimensional Prompt Tuning via Random Projection](https://md.research.elamur.ai/free/en/papers/wu-2026-ultra-low-dimensional-prompt-tuning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zijun Wu, Yongchang Hao, Lili Mou*2026*
23. [**Can predicate-argument relationships be extracted from UD trees?**Adam Ek, Jean-Philippe Bernardy, Stergios Chatzikyriakidis*2021*](https://md.research.elamur.ai/free/en/papers/ek-2021-can-predicate-argument-relationships-be/)

84 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/demszky-2018-transforming-question-answering-datasets-natural/) — Transforming Question Answering Datasets Into Natural Language Inference Datasets