mlsanityData to 7 Oct 2026

DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension

arXiv 1902.00164 ↗First posted 1 Feb 2019cs.CLcited by 156

Not on the map. Why?

Abstract (arXiv)

We present DREAM, the first dialogue-based multiple-choice reading comprehension dataset. Collected from English-as-a-foreign-language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our dataset contains 10,197 multiple-choice questions for 6,444 dialogues. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge. We apply several popular neural reading comprehension models that primarily exploit surface information within the text and find them to, at best, just barely outperform a rule-based approach. We next investigate the effects of incorporating dialogue structure and different kinds of general world knowledge into both rule-based and (neural and non-neural) machine learning-based reading comprehension models. Experimental results on the DREAM dataset show the effectiveness of dialogue structure and general world knowledge. DREAM will be available at https://dataset.org/dream/.

Key facts. Cited by 156 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Feb 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 32; 2022: 49; 2023: 39; 2024: 24; 2025: 8; 2026: 4.

156 papers.

Sort:

By area:

  1. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  2. 2021MetaICL: Learning to Learn In Contextcited by 450
  3. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
  4. 2022Leveraging Large Language Models for Multiple Choice Question Answeringcited by 199
  5. 2021DialogSum: A Real-Life Scenario Dialogue Summarization Datasetcited by 181
  6. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  7. 2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
  8. 2024AudioBench: A Universal Benchmark for Audio Large Language Modelscited by 124
  9. 2023The Generative AI Paradox: "What It Can Create, It May Not Understand"cited by 97
  10. 2023SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoningcited by 94
  11. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  12. 2023Exploring the Benefits of Training Expert Language Models over Instruction Tuningcited by 80
38 more
  1. 2024Instruction Pre-Training: Language Models are Supervised Multitask Learnerscited by 63
  2. 2022UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Trainingcited by 57
  3. 2021Zero-Shot Dialogue State Tracking via Cross-Task Transfercited by 52
  4. 2024What Matters for Model Merging at Scale?cited by 52
  5. 2023Pre-Training to Learn in Contextcited by 41
  6. 2023Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agentscited by 41
  7. 2022CICERO: A Dataset for Contextualized Commonsense Inference in Dialoguescited by 38
  8. 2021Reason first, then respond: Modular Generation for Knowledge-infused Dialoguecited by 32
  9. 2021Relation-aware Video Reading Comprehension for Temporal Language Groundingcited by 27
  10. 2021English Machine Reading Comprehension Datasets: A Surveycited by 26
  11. 2024Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluationcited by 25
  12. 2022Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Modelscited by 24
  13. 2024On the Universal Truthfulness Hyperplane Inside LLMscited by 23
  14. 2022MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoningcited by 22
  15. 2022Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learnerscited by 22
  16. 2022VASR: Visual Analogies of Situation Recognitioncited by 22
  17. 2022ProQA: Structural Prompt-based Pre-training for Unified Question Answeringcited by 21
  18. 2024Weak-to-Strong Generalization Through the Data-Centric Lenscited by 21
  19. 2021Structural Pre-training for Dialogue Comprehensioncited by 20
  20. 2021CIDER: Commonsense Inference for Dialogue Explanation and Reasoningcited by 20
  21. 2024SpeechPrune: Context-aware Token Pruning for Speech Information Retrievalcited by 20
  22. 2023Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Textcited by 19
  23. 2021QAConv: Question Answering on Informative Conversationscited by 17
  24. 2022RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Questioncited by 17
  25. 2022ALERT: Adapting Language Models to Reasoning Taskscited by 17
  26. 2021Few Shot Dialogue State Tracking using Meta-learningcited by 16
  27. 2021DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehensioncited by 16
  28. 2021NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulationcited by 16
  29. 2022MVP: Multi-task Supervised Pre-training for Natural Language Generationcited by 16
  30. 2021CoMPS: Continual Meta Policy Searchcited by 15
  31. 2022Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalizationcited by 15
  32. 2023VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Modelscited by 15
  33. 2024Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcomcited by 15
  34. 2021NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Datasetcited by 14
  35. 2022Task Compass: Scaling Multi-task Pre-training with Task Prefixcited by 14
  36. 2021Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehensioncited by 13
  37. 2021Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehensioncited by 13
  38. 2022Towards Fine-grained Causal Reasoning and QAcited by 13

50 most cited of 156 All 156 (JSON)