We propose to use reinforcement learning to inform transformer-based contextualized link prediction models by providing paths that are most useful for predicting the correct answer. This is in contrast to previous approaches, that either used reinforcement learning (RL) to directly search for the answer, or based their prediction on limited or randomly selected context. Our experiments on WN18RR and FB15k-237 show that contextualized link prediction models consistently outperform RL-based answer search, and that additional improvements (of up to 13.5\% MRR) can be gained by combining RL with a link prediction model.
翻译:我们建议利用强化学习向基于变压器的背景化链接预测模型提供信息,方法是提供对预测正确答案最有用的路径。这与以往的做法不同,以往的方法是使用强化学习直接搜索答案,或者根据有限或随机选择的背景进行预测。 我们在WN18RR和FB15k-237的实验显示,基于背景化的预测模型始终优于基于RL的回答搜索,如果将RL与链接预测模型结合起来,可以取得进一步的改进(最高为13.5 ⁇ MRR)。