SQuAD: 100,000+ Questions for Machine Comprehension of Text

Led to

• DrQA2017 · cited 6×
  “That subfield has made considerable progress recently thanks to new deep learning architectures like attention-based and memory-augmented neural networks Bahdanau et al. 2015; Weston et al. 2015; Graves et al. 2014 and r…”

  From DrQA · §Related Work
• CoQA2018 · cited 3×, 1 in Method
  “We use the Document Reader (DrQA) model of Chen et al. 2017, which has demonstrated strong performance on multiple datasets Rajpurkar et al. 2016; Labutov et al. 2018.”

  From CoQA · §Models
• BERT2018 · cited 3×
  “When integrating contextual word embeddings with existing task-specific architectures, ELMo advances the state of the art for several major NLP benchmarks Peters et al. 2018a including question answering Rajpurkar et al.…”

  From BERT · §Related Work
• ReCoRD2018 · cited 5×
  “Since all candidate answers were extracted from in the passage, ReCoRD can also be formalized as a extractive MRC dataset, similar to SQuAD Rajpurkar et al. 2016 and NewsQA Trischler et al. 2017.”

  From ReCoRD · §Related Datasets
• UniLM2019 · cited 3×
  “The task is to answer a question given a passage [33, 34, 15].”

  From UniLM · §Experiments
• BoolQ2019 · cited 2×, 1 in Method
  “First, we use the QNLI task from GLUE Wang et al. 2018, where the model must determine if a sentence from SQuAD 1.1 Rajpurkar et al. 2016 contains the answer to an input question or not.”

  From BoolQ · §Training Yes/No QA Models
• RoBERTa2019 · cited 2×, 2 in Method
  “The General Language Understanding Evaluation (GLUE) benchmark Wang et al. 2019b is a collection of 9 datasets for evaluating natural language understanding systems.66 6 The datasets are: CoLA Warstadt et al. 2018, Stanf…”

  From RoBERTa · §Experimental Setup
• T52019 · cited 3×, 2 in Method
  “Natural language inference (MNLI (Williams et al. 2017), QNLI (Rajpurkar et al. 2016), RTE (Dagan et al. 2005), CB (De Marneff et al. 2019))”

  From T5 · §Setup
• BART2019 · cited 2×, 1 in Method
  “Rajpurkar et al. 2016a an extractive question answering task on Wikipedia paragraphs.”

  From BART · §Comparing Pre-training Objectives
• Knowledge in LM parameters2020 · cited 4×, 2 in Method
  “Most past work on question answering either explicitly feeds pertinent information to the model alongside the question (for example, an article that contains the answer Rajpurkar et al. 2016; Zhang et al. 2018; Khashabi…”

  From Knowledge in LM parameters · §Introduction
• BIG-bench2022 · cited 8×
  “For instance, benchmarks often propose tasks that codify narrow subsets of areas, such as language understanding (Wang et al. 2019a), summarization (See et al. 2017; Hermann et al. 2015; Narayan et al. 2018; Koupaee & Wa…”

  From BIG-bench · §Introduction