通过有意义学习重新审视系统概括
Revisit Systematic Generalization via Meaningful Learning
摘要：
人类可以系统地概括现有概念的新组合。最近的研究认为，神经网络在这种认知能力上似乎天生无效，导致悲观的观点和对乐观结果的关注不足。我们从有意义学习的角度重新审视这个有争议的话题，这是人类通过将新概念与已知概念联系起来来学习新概念的一种特殊能力。我们重新评估的组合技能的序列到序列模型的条件下，新的和旧的概念之间的语义联系。我们的观察表明，模型可以成功地一次性概括到新的概念和成分，通过语义链接，无论是归纳或演绎。我们证明了先验知识也起着关键作用。除了综合测试，我们还在机器翻译和语义解析中进行了概念验证实验，展示了有意义学习在应用中的好处。我们希望我们的积极发现将鼓励挖掘现代神经网络的潜力，通过更先进的学习计划的系统推广。
英文摘要：
Humans can systematically generalize to novel compositions of existing concepts. Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity, leading to a pessimistic view and a lack of attention to optimistic results. We revisit this controversial topic from the perspective of meaningful learning, an exceptional capability of humans to learn novel concepts by connecting them with known ones. We reassess the compositional skills of sequence-to-sequence models conditioned on the semantic links between new and old concepts. Our observations suggest that models can successfully one-shot generalize to novel concepts and compositions through semantic linking, either inductively or deductively. We demonstrate that prior knowledge plays a key role as well. In addition to synthetic tests, we further conduct proof-of-concept experiments in machine translation and semantic parsing, showing the benefits of meaningful learning in applications. We hope our positive findings will encourage excavating modern neural networks' potential in systematic generalization through more advanced learning schemes.
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。
