Solution of DeBERTaV3 on CommonsenseQA
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Abstract (arXiv)
We report the performance of DeBERTaV3 on CommonsenseQA in this report. We simply formalize the answer selection as a text classification for DeBERTaV3. The strong natural language inference ability of DeBERTaV3 helps its single and ensemble model set the new (w/o external knowledge) state-of-the-art on CommonsenseQA.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 30 Apr 2022.
Builds on
8 of 10 references.
References · 8
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
- 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
- 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
- 2020UnifiedQA: Crossing Format Boundaries With a Single QA Systemcited by 475
- 2019FreeLB: Enhanced Adversarial Training for Natural Language Understandingcited by 261
- 2020Fusing Context Into Knowledge Graph for Commonsense Question Answeringcited by 33
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