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# Who cites «Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences» · page 2

299 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/khashabi-2018-looking-beyond-surface-challenge-set/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set-3/)

1. [UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation](https://aclanthology.org/2023.emnlp-main.758/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
2. [UltraFeedback: Boosting Language Models with Scaled AI Feedback](https://arxiv.org/abs/2310.01377) *— 2023 · arXiv:2310.01377*
3. [Understanding the Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance?](https://aclanthology.org/2023.emnlp-main.563/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
4. [Unsupervised Selective Rationalization with Noise Injection](https://aclanthology.org/2023.acl-long.707/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [Vote''n''Rank: Revision of Benchmarking with Social Choice Theory](https://aclanthology.org/2023.eacl-main.48/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
6. [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://arxiv.org/abs/2312.09390) *— 2023 · arXiv:2312.09390*
7. [What's the Meaning of Superhuman Performance in Today's NLU?](https://aclanthology.org/2023.acl-long.697/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle](https://md.mova.elamur.ai/free/en/docs/papers/sieker-2023-when-your-language-model-cannot/) *— our card · 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
9. [Where to start? Analyzing the potential value of intermediate models](https://aclanthology.org/2023.emnlp-main.90/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
10. [ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics](https://md.mova.elamur.ai/free/en/docs/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/) *— our card · 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
11. [ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://aclanthology.org/2022.emnlp-main.446/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
12. [AdapLeR: Speeding up Inference by Adaptive Length Reduction](https://aclanthology.org/2022.acl-long.1/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [An Investigation of the (In)effectiveness of Counterfactually Augmented Data](https://aclanthology.org/2022.acl-long.256/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Analyzing Multi-Task Learning for Abstractive Text Summarization](https://aclanthology.org/2022.gem-1.5/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
15. [Ask Me Anything: A simple strategy for prompting language models](https://arxiv.org/abs/2210.02441) *— 2022 · arXiv:2210.02441*
16. [BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types](https://aclanthology.org/2022.findings-emnlp.186/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
17. [Can Rationalization Improve Robustness?](https://aclanthology.org/2022.naacl-main.278/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
18. [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://aclanthology.org/2022.acl-long.244/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [Design Challenges for a Multi-Perspective Search Engine](https://aclanthology.org/2022.findings-naacl.22/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
20. [Double Trouble: How to not Explain a Text Classifier''s Decisions Using Counterfactuals Synthesized by Masked Language Models?](https://aclanthology.org/2022.aacl-main.2/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
21. [Down and Across: Introducing Crossword-Solving as a New NLP Benchmark](https://aclanthology.org/2022.acl-long.189/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts](https://aclanthology.org/2022.findings-emnlp.189/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
23. [FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding](https://aclanthology.org/2022.acl-long.38/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://aclanthology.org/2022.acl-long.592/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [Hey AI, Can You Solve Complex Tasks by Talking to Agents?](https://aclanthology.org/2022.findings-acl.142/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
26. [Human-Centered Evaluation of Explanations](https://aclanthology.org/2022.naacl-tutorials.4/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts*
27. [Improving In-Context Few-Shot Learning via Self-Supervised Training](https://aclanthology.org/2022.naacl-main.260/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
28. [Is a Question Decomposition Unit All We Need?](https://aclanthology.org/2022.emnlp-main.302/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
29. [Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models](https://aclanthology.org/2022.findings-emnlp.121/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
30. [Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering](https://aclanthology.org/2022.coling-1.142/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
31. [On the Diversity and Limits of Human Explanations](https://aclanthology.org/2022.naacl-main.158/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
32. [Reframing Instructional Prompts to GPTk's Language](https://aclanthology.org/2022.findings-acl.50/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
33. [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://aclanthology.org/2022.acl-long.346/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://aclanthology.org/2022.emnlp-main.131/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
35. [Transferring Confluent Knowledge to Argument Mining](https://aclanthology.org/2022.coling-1.597/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
36. [UKP-SQUARE: An Online Platform for Question Answering Research](https://aclanthology.org/2022.acl-demo.2/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
37. [UNIREX: A Unified Learning Framework for Language Model Rationale Extraction](https://aclanthology.org/2022.bigscience-1.5/) *— 2022 · Proceedings of BigScience Episode \#5 – Workshop on Challenges & Perspectives in Creating Large Language Models*
38. [Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding](https://aclanthology.org/2022.emnlp-main.455/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
39. [What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?](https://arxiv.org/abs/2204.05832) *— 2022 · arXiv:2204.05832*
40. [What Language Model to Train if You Have One Million GPU Hours?](https://arxiv.org/abs/2210.15424) *— 2022 · arXiv:2210.15424*
41. [What Makes Reading Comprehension Questions Difficult?](https://aclanthology.org/2022.acl-long.479/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [What to Learn, and How: Toward Effective Learning from Rationales](https://aclanthology.org/2022.findings-acl.86/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
43. [Word Order Does Matter and Shuffled Language Models Know It](https://aclanthology.org/2022.acl-long.476/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
44. [XPrompt: Exploring the Extreme of Prompt Tuning](https://aclanthology.org/2022.emnlp-main.758/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
45. [Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://aclanthology.org/2022.emnlp-main.474/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
46. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
47. [Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization](https://aclanthology.org/2021.findings-emnlp.37/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
48. [Conditional Generation of Temporally-ordered Event Sequences](https://aclanthology.org/2021.acl-long.555/) *— 2021 · 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)*
49. [Coreference Reasoning in Machine Reading Comprehension](https://aclanthology.org/2021.acl-long.448/) *— 2021 · 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)*
50. [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://arxiv.org/abs/2104.08835) *— 2021 · arXiv:2104.08835*
51. [DAGN: Discourse-Aware Graph Network for Logical Reasoning](https://aclanthology.org/2021.naacl-main.467/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
52. [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://aclanthology.org/2021.tacl-1.21/) *— 2021*
53. [Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?](https://aclanthology.org/2021.eacl-main.283/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
54. [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://arxiv.org/abs/2107.02137) *— 2021 · arXiv:2107.02137*
55. [English Machine Reading Comprehension Datasets: A Survey](https://md.mova.elamur.ai/free/en/docs/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/) *— our card · 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
56. [Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience](https://aclanthology.org/2021.emnlp-main.645/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
57. [Evaluation Paradigms in Question Answering](https://aclanthology.org/2021.emnlp-main.758/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
58. [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952) *— 2021 · arXiv:2111.10952*
59. [FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation](https://aclanthology.org/2021.emnlp-main.301/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
60. [Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652) *— 2021 · arXiv:2109.01652*
61. [GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation](https://aclanthology.org/2021.findings-acl.113/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
62. [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://arxiv.org/abs/2112.06905) *— 2021 · arXiv:2112.06905*
63. [GPT Understands, Too](https://arxiv.org/abs/2103.10385) *— 2021 · arXiv:2103.10385*
64. [How many data points is a prompt worth?](https://aclanthology.org/2021.naacl-main.208/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
65. [If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering](https://aclanthology.org/2021.naacl-main.363/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
66. [It''s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://aclanthology.org/2021.naacl-main.185/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
67. [Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization](https://aclanthology.org/2021.naacl-main.104/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
68. [Measuring Association Between Labels and Free-Text Rationales](https://aclanthology.org/2021.emnlp-main.804/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
69. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
70. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
71. [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://aclanthology.org/2021.emnlp-main.468/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
72. [On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings](https://aclanthology.org/2021.emnlp-main.596/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
73. [Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://aclanthology.org/2021.naacl-main.22/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
74. [REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training](https://aclanthology.org/2021.findings-acl.13/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
75. [SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning](https://md.mova.elamur.ai/free/en/docs/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/) *— our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
76. [Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models](https://aclanthology.org/2021.naacl-main.99/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
77. [The Power of Scale for Parameter-Efficient Prompt Tuning](https://aclanthology.org/2021.emnlp-main.243/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
78. [True Few-Shot Learning with Language Models](https://arxiv.org/abs/2105.11447) *— 2021 · arXiv:2105.11447*
79. [UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark](https://arxiv.org/abs/2103.13009) *— 2021 · arXiv:2103.13009*
80. [What to Pre-Train on? Efficient Intermediate Task Selection](https://aclanthology.org/2021.emnlp-main.827/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
81. [A Diagnostic Study of Explainability Techniques for Text Classification](https://aclanthology.org/2020.emnlp-main.263/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
82. [A Framework for Evaluation of Machine Reading Comprehension Gold Standards](https://aclanthology.org/2020.lrec-1.660/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
83. [A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction](https://aclanthology.org/2020.acl-main.361/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
84. [An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction](https://aclanthology.org/2020.emnlp-main.153/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
85. [Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options](https://aclanthology.org/2020.aacl-main.68/) *— 2020 · Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing*
86. [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) *— 2020 · arXiv:2006.03654*
87. [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://aclanthology.org/2020.acl-main.408/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
88. [Evaluating and Characterizing Human Rationales](https://aclanthology.org/2020.emnlp-main.747/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
89. [Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings](https://aclanthology.org/2020.coling-main.275/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
90. [HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://aclanthology.org/2020.findings-emnlp.309/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
91. [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://aclanthology.org/2020.emnlp-main.437/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
92. [INFOTABS: Inference on Tables as Semi-structured Data](https://aclanthology.org/2020.acl-main.210/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
93. [Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://aclanthology.org/2020.acl-main.467/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
94. [Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning](https://aclanthology.org/2020.emnlp-main.712/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
95. [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) *— 2020 · arXiv:2005.14165*
96. [Learning from Task Descriptions](https://aclanthology.org/2020.emnlp-main.105/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
97. [Learning to Faithfully Rationalize by Construction](https://aclanthology.org/2020.acl-main.409/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
98. [Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions](https://aclanthology.org/2020.lrec-1.217/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
99. [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://aclanthology.org/2020.emnlp-main.528/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
100. [More Bang for Your Buck: Natural Perturbation for Robust Question Answering](https://aclanthology.org/2020.emnlp-main.12/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*

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