Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)
This addresses the need for more intelligent information retrieval systems that can synthesize content, though it is a preliminary study with incremental steps.
The paper tackles the problem of generating relevant information rather than retrieving it by training an RNN on existing relevant documents to synthesize a single document for a query, and in a crowdsourcing experiment, the synthetic document was ranked most relevant on average compared to three existing documents.
What if Information Retrieval (IR) systems did not just retrieve relevant information that is stored in their indices, but could also "understand" it and synthesise it into a single document? We present a preliminary study that makes a first step towards answering this question. Given a query, we train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all.