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2020.acl-main.441.pdf
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🔗 Source: aclanthology.org
📊 Size: 1.38 MB
👤 Author: Yixin Nie ; Adina Williams ; Emily Dinan ; Mohit Bansal ; Jason Weston ; Douwe Kiela
⬇️ Downloads: 299

Summary

Researchers introduce Adversarial NLI, a new large-scale benchmark dataset for natural language understanding, collected via an iterative human-and-model-in-the-loop procedure. The dataset is designed to be more challenging than existing benchmarks and can be used to train models that achieve state-of-the-art performance on various NLI tasks. The data collection method involves human annotators creating examples that current models struggle with, which are then verified and used to train stronger models. This process can be repeated to create a dynamic target for NLU systems, rather than a static benchmark that becomes obsolete quickly. The resulting dataset, ANLI, consists of three rounds of data that increase in difficulty and complexity, and includes annotator-provided explanations.

Description

Researchers introduce Adversarial NLI, a new large-scale benchmark dataset for natural language understanding, collected via an iterative...

Technical Information

  • File Format: PDF
  • File Size: 1.38 MB
  • Pages: 17
  • Language: EN
  • Author: Yixin Nie ; Adina Williams ; Emily Dinan ; Mohit Bansal ; Jason Weston ; Douwe Kiela
  • Total Downloads: 299
  • Last Updated: 4 weeks ago

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