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Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

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Figure

Figure 1: To answer a question posed in naturallanguage, GRAFT-Net considers a heterogeneousgraph constructed from text and KB facts, and thuscan leverage the rich relational structure betweenthe two information sources.
Figure 3: Directed propagation of embeddings inGRAFT-Net. A scalar PageRank score pr(l)vismaintained for each node v across layers, whichspreads out from the seed node
Table 1: Statistics of all the retrieved subgraphs ∪qGq for WikiMovies-10K and WebQuestionsSP.
Table 3: Examples from WebQuestionsSP dataset. Top: The model misses a correct answer

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