We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection\/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding\u2019s advantage over other strategies for encoding the metadata.<\/p>\n","protected":false},"excerpt":{"rendered":"
We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection\/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. 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