QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model
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Abstract (arXiv)
In this paper, we present language model system submitted to SemEval-2020 Task 4 competition: "Commonsense Validation and Explanation". We participate in two subtasks for subtask A: validation and subtask B: Explanation. We implemented with transfer learning using pretrained language models (BERT, XLNet, RoBERTa, and ALBERT) and fine-tune them on this task. Then we compared their characteristics in this task to help future researchers understand and use these models more properly. The ensembled model better solves this problem, making the model's accuracy reached 95.9% on subtask A, which just worse than human's by only 3% accuracy.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Sep 2020.
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Citing papers per year: 2022: 2; 2023: 1.
3 papers.
- 2023Speculative Decoding with Big Little Decodercited by 164
- 2022Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanationcited by 2
- 2022UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentationcited by 1
- 2023Speculative Decoding with Big Little Decoder15 Feb 2023
- 2022UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation7 Oct 2022
- 2022Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanation7 Apr 2022