ArT: All-round Thinker for Unsupervised Commonsense Question Answering (2022.coling-1)
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| Challenge: | Existing work on commonsense QA requires labeled training data for its success . existing work relies on large-scale in-domain or out-of-domain labeles or fails to generate knowledge of high quality in a general way. |
| Approach: | They propose an approach to commonsense question-answering (QA) that takes association during knowledge generation. |
| Outcome: | The proposed model outperforms existing models on commonsense QA benchmarks. |
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