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Inspired by the notion that ``{\it to copy is easier than to memorize}``, inthis work, we introduce GNN-LM, which extends the vanilla neural language model(LM) by allowing to reference similar contexts in the entire training corpus.We build a directed heterogeneous graph between an input context and itssemantically related neighbors selected from the training corpus, where nodesare tokens in the input context and retrieved neighbor contexts, and edgesrepresent connections between nodes. Graph neural networks (GNNs) areconstructed upon the graph to aggregate information from similar contexts todecode the token. This learning paradigm provides direct access to thereference contexts and helps improve a model's generalization ability. Weconduct comprehensive experiments to validate the effectiveness of the GNN-LM:GNN-LM achieves a new state-of-the-art perplexity of 14.8 on WikiText-103 (a3.9 point improvement over its counterpart of the vanilla LM model), and showssubstantial improvement on One Billion Word and Enwiki8 datasets against strongbaselines. In-depth ablation studies are performed to understand the mechanicsof GNN-LM. \footnote{The code can be found athttps://github.com/ShannonAI/GNN-LM

GNN-LM: Language Modeling based on Global Contexts via GNN