Analyzing Transferable Knowledge by Pretraining with Artificial Language

Acquisition of Knowledge Data by Analyzing Natural Language

On the Effective Use of Pretraining for Natural Language Inference

Neural networks have excelled at many NLP tasks, but there remain open questions about the performance of pretrained distributed word representations and their interaction with weight initialization and other hyperparameters. We address these questions empirically using attention-based sequence-tosequence models for natural language inference (NLI). Specifically, we compare three types of embed...

Affordances as Transferable Knowledge for Planning Agents

Robotic agents often map perceptual input to simplified representations that do not reflect the complexity and richness of the world. This simplification is due in large part to the limitations of planning algorithms, which fail in large stochastic state spaces on account of the well-known “curse of dimensionality.” Existing approaches to address this problem fail to prevent autonomous agents f...

Analyzing the tradition, hadith, of self-knowledge with regard to the knowledge of God

  Deep thinking on the tradition and analyzing other tradition dealing with knowledge of human soul distinguish this proof from those of philosophers and theologians. Availability, unity of the path and the follower and, most particularly, its being intuitive enter the follower to an objective state and, contrary to conceptual proofs, leads him/her to the presence of God. Again, this traditio...

The acquisition of morphological knowledge investigated through artificial language learning.

Affix knowledge plays an important role in visual word recognition, but little is known about how it is acquired. The authors present a new method of investigating the acquisition of affixes in which participants are trained on novel affixes presented in novel word contexts (e.g., sleepnept). Experiment 1 investigated the role of semantic information on affix acquisition by comparing a form-lea...