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Vector-space models, from word embeddings to neural network parsers, havemany advantages for NLP. But how to generalise from fixed-length word vectorsto a vector space for arbitrary linguistic structures is still unclear. In thispaper we propose bag-of-vector embeddings of arbitrary linguistic graphs. Abag-of-vector space is the minimal nonparametric extension of a vector space,allowing the representation to grow with the size of the graph, but not tyingthe representation to any specific tree or graph structure. We proposeefficient training and inference algorithms based on tensor factorisation forembedding arbitrary graphs in a bag-of-vector space. We demonstrate theusefulness of this representation by training bag-of-vector embeddings ofdependency graphs and evaluating them on unsupervised semantic induction forthe Semantic Textual Similarity and Natural Language Inference tasks.

Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction