儿童文本是常识知识的关键吗？
Do Children Texts Hold The Key To Commonsense Knowledge?
摘要：
编译常识知识的综合存储库是人工智能领域的一个长期问题。许多关切围绕着报告偏见问题，即，文本来源中的频率并不能很好地代表相关性或真实性。本文基于儿童文本对读者知识的假设较少，因此更明确地阐述了常识的假设，探讨了儿童文本是否掌握了常识知识汇编的关键。对多个语料库的分析表明，儿童语篇中确实包含了更多、更典型的常识性断言。此外，实验表明，这种优势可以利用流行的基于语言模型的常识知识提取设置，其中任务非特定的微调少量的儿童文本（childBERT）已经产生显着的改善。这提供了一个令人耳目一新的视角，不同于从越来越大的模型和语料库中获得进展的共同趋势。
英文摘要：
Compiling comprehensive repositories of commonsense knowledge is a long-standing problem in AI. Many concerns revolve around the issue of reporting bias, i.e., that frequency in text sources is not a good proxy for relevance or truth. This paper explores whether children's texts hold the key to commonsense knowledge compilation, based on the hypothesis that such content makes fewer assumptions on the reader's knowledge, and therefore spells out commonsense more explicitly. An analysis with several corpora shows that children's texts indeed contain much more, and more typical commonsense assertions. Moreover, experiments show that this advantage can be leveraged in popular language-model-based commonsense knowledge extraction settings, where task-unspecific fine-tuning on small amounts of children texts (childBERT) already yields significant improvements. This provides a refreshing perspective different from the common trend of deriving progress from ever larger models and corpora.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
