Abstract
We show that they do. Schank's conceptual dependency theory proposed that all events decompose into primitive operations -- ATRANS, PTRANS, MTRANS, and others -- hand-coded from linguistic intuition. Can the same primitives be discovered automatically through compression pressure alone? We adapt DreamCoder's wake-sleep library learning to event state transformations. Given events as before/after world state pairs, our system finds operator compositions explaining each event (wake), then extracts recurring patterns as new operators optimized under Minimum Description Length (sleep). Starting from four generic primitives, it discovers operators mapping directly to Schank's: MOVE_PROP_has = ATRANS, CHANGE_location = PTRANS, SET_knows = MTRANS, SET_consumed = INGEST, plus compound operators ("mail" = ATRANS + PTRANS) and novel emotional state operators absent from Schank's taxonomy. We validate on synthetic events and real-world commonsense data from the ATOMIC knowledge graph. On synthetic data, discovered operators achieve Bayesian MDL within 4% of Schank's hand-coded primitives while explaining 100% of events vs. Schank's 81%. On ATOMIC, results are more dramatic: Schank's primitives explain only 10% of naturalistic events, while the discovered library explains 100%. Dominant operators are not physical-action primitives but mental and emotional state changes -- CHANGE_wants (20%), CHANGE_feels (18%), CHANGE_is (18%) -- none in Schank's original taxonomy. These results provide the first empirical evidence that event primitives can be derived from compression pressure, that Schank's core primitives are information-theoretically justified, and that the complete inventory is substantially richer than proposed -- with mental/emotional operators dominating in naturalistic data.
Abstract (translated)
我们证明了它们确实可以。尚克的概念依赖理论提出所有事件都可分解为原语操作——如ATRANS、PTRANS、MTRANS等——这些操作基于语言学直觉手工编码。能否仅通过压缩压力自动发现相同的原语？我们改编了DreamCoder的唤醒-睡眠库学习机制用于事件状态转换。给定事件的前后世界状态对，我们的系统先为每个事件寻找解释性算子组合（唤醒阶段），再提取重复模式作为新算子，并在最小描述长度原则下进行优化（睡眠阶段）。从四个通用原语出发，系统发现了与尚克原语直接对应的算子：MOVE_PROP_has = ATRANS，CHANGE_location = PTRANS，SET_knows = MTRANS，SET_consumed = INGEST，同时还发现了复合算子（如"mail" = ATRANS + PTRANS）以及尚克分类中缺失的情感状态算子。我们在合成事件和ATOMIC知识图谱的真实常识数据上进行了验证。在合成数据上，发现的算子在贝叶斯MDL指标上仅与尚克手工原语相差4%，却能解释100%的事件（尚克原语仅解释81%）。在ATOMIC数据上结果更为显著：尚克原语仅能解释10%的自然事件，而发现的算子库实现了100%解释。主导算子并非物理动作原语，而是心理与情感状态变化——CHANGE_wants（20%）、CHANGE_feels（18%）、CHANGE_is（18%）——这些均未出现在尚克原始分类中。这些结果首次提供实证证据：事件原语可从压缩压力中推导，尚克核心原语具有信息论依据，且完整原语库比原设想更为丰富——在自然数据中，心理/情感算子占据主导地位。
URL
https://arxiv.org/abs/2603.25975
