27,780 research outputs found

    Integrating Guidance into Relational Reinforcement Learning

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    Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

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    We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201

    Reclaiming Sacred Space

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    I wrote this piece for myself as a hybrid of personal discovery and academic inquiry, and I hope it can guide and empower others like myself. In this piece, I examine the intersections of queer identity with religious and spiritual identity development and discuss how practitioners can help students reclaim sacred space. Foregrounding my personal narrative and expanding with scholarship, I show why this development deserves attention from student affairs professionals. I give both programmatic and institutional considerations to review when centering religious and spiritual development for LGBTQ students
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