On the Possibility of Rewarding Structure Learning Agents: Mutual Information on Linguistic Random Sets
This work addresses a foundational challenge in unsupervised learning for natural language processing, though it appears incremental as it builds on existing information theory frameworks.
The paper tackled the problem of designing rewards for structure learning agents in natural language environments by using mutual information to distinguish simulated semantic structures from random ones, showing that Open Information Extraction triplets can be identified without pretrained analyzers.
We present a first attempt to elucidate a theoretical and empirical approach to design the reward provided by a natural language environment to some structure learning agent. To this end, we revisit the Information Theory of unsupervised induction of phrase-structure grammars to characterize the behavior of simulated actions modeled as set-valued random variables (random sets of linguistic samples) constituting semantic structures. Our results showed empirical evidence of that simulated semantic structures (Open Information Extraction triplets) can be distinguished from randomly constructed ones by observing the Mutual Information among their constituents. This suggests the possibility of rewarding structure learning agents without using pretrained structural analyzers (oracle actors/experts).