Datasets › CoQA
CoQA (Conversational Question Answering Challenge)
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.
CoQA contains 127,000+ questions with answers collected from 8000+ conversations. Each conversation is collected by pairing two crowdworkers to chat about a passage in the form of questions and answers. The unique features of CoQA include 1) the questions are conversational; 2) the answers can be free-form text; 3) each answer also comes with an evidence subsequence highlighted in the passage; and 4) the passages are collected from seven diverse domains. CoQA has a lot of challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning.
Source: https://stanfordnlp.github.io/coqa/ Image Source: https://stanfordnlp.github.io/coqa/
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Question Answering | CoQA | BERT Large Augmented (single model) In-domain 82.5 | BERT: Pre-training of Deep Bidirectional Transformers... | huggingface/transformers +533 | 9 | Compare |
| Generative Question Answering | CoQA | ERNIE-GEN F1-Score 84.5 | ERNIE-GEN: An Enhanced Multi-Flow Pre-training and... | PaddlePaddle/PaddleNLP +4 | 3 | Compare |
Papers archive 2025-07-28
8 shown of 8 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 281. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Language Models are Few-Shot Learners | 67 | 1 | 28 May 2020 | ran 15 of 65 samples (50 unverified; 4 pointer-only for licence) |
| ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation | 5 | 1 | 26 Jan 2020 | not harvested |
| Unified Language Model Pre-training for Natural Language Understanding and Generation | 9 | 1 | 8 May 2019 | not harvested |
| SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering | 6 | 2 | 10 Dec 2018 | not harvested |
| BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | 534 | 2 | 11 Oct 2018 | ran 204 of 659 samples (455 unverified; 149 pointer-only for licence) |
| FlowQA: Grasping Flow in History for Conversational Machine Comprehension | 1 | 1 | 6 Oct 2018 | not harvested |
| A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC | 1 | 1 | 27 Sep 2018 | not harvested |
| CoQA: A Conversational Question Answering Challenge | 4 | 3 | 21 Aug 2018 | ran 2 of 2 samples (0 unverified) |
Dataset loaders archive 2025-07-28
6 loaders as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- CoQA
1 variant name, as the archive lists them.
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