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ReInform: Selecting paths with reinforcement learning for contextualized link prediction

arXiv 2211.10688 ↗First posted 19 Nov 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

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. The PyTorch implementation of the RL agent is available at https://github.com/marina-sp/reinform

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 19 Nov 2022.

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References · 15

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3 more
  1. 2018DOLORES: Deep Contextualized Knowledge Graph Embeddingscited by 11
  2. 2019Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answeringcited by 7
  3. 2020Explainable Link Prediction for Emerging Entities in Knowledge Graphscited by 1

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