KARL: Knowledge-Aware Reasoning Memory Modeling with Reinforcement Learning of Vector SpaceDownload PDF

16 Dec 2019 (modified: 22 Jun 2020)OpenReview Anonymous Preprint Blind SubmissionReaders: Everyone
  • Keywords: cognitive modeling, knowledge graph, reasoning, knowledge-based question answering
  • Abstract: Founded in Atkinson-Shiffrin Memory Model's three-stage theory, the cognitive process of answering a question with stored knowledge can be seen as a reasoning process that goes from the external sensory memory via short-term knowledge-aware reasoning towards the internal knowledge storage. While in the machine, this process can be interpreted as a pipeline from encoding the question’s information, through decoding the reasoning query of triples, towards the knowledgebase. How to encode and decode the varying semantic information of question into accurate triple query, and how to adjust the query generation with an evolving knowledgebase, are two inevitable problems in this cognitive process. Our model KARL, provides a solution by designing the three memory spaces as, an encoder to handle the language modeling, a decoder for query generation, and a self-calibration with reinforcement learning of the knowledge representation vector space. We evaluate the model's reasoning ability in knowledge-based question answering against the Question Answering over Linked Data (QALD) benchmark, and achieve significant improvements in answers accuracy as compared to other neural models.
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