CoQA Dataset

A CoQA Dataset is a QA dataset that is a large-scale dataset for building Conversational Question Answering Systems.

• AKA: CoQA Corpus, Conversation Question Answering (CoQA) Dataset.
• Contex:
  • Datasets available at: https://stanfordnlp.github.io/coqa/
  • Benchmarking Task: CoQA Challenge.
  • It contains 127,000+ questions with answers collected from 8000+ conversations.
• Example(s):
  • Training Dataset: https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json,
  • Development Dataset: https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json.
  • …
• Counter-Example(s):
  • a FastQA Dataset,
  • a GLUE Dataset.
  • a ImageNet Dataset,
  • a MS COCO Dataset,
  • a NarrativeQA Dataset,
  • a NewsQA Dataset,
  • a RACE Dataset,
  • a SearchQA Dataset,
  • a SQuAD Dataset.
• See: Question-Answering System, Natural Language Processing Task, Natural Language Understanding Task, Natural Language Generation Task.

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References

2020

• (CoQA, 2020) ⇒ https://stanfordnlp.github.io/coqa/ Retrieved:2020-06-03.
  • QUOTE: CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation.

2019

• (Reddy et al., 2019) ⇒ Siva Reddy, Danqi Chen, and Christopher D. Manning. (2019). “CoQA: A Conversational Question Answering Challenge.” In: Transactions of the Association for Computational Linguistics Journal, 7. DOI:10.1162/tacl_a_00266.
  • QUOTE: We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets (e.g., coreference and pragmatic reasoning). We evaluate strong dialogue and reading comprehension models on CoQA.

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