How to Train Your Agent to Read and Write

• URL: http://arxiv.org/abs/2101.00916v1
• Date: Mon, 4 Jan 2021 12:22:04 GMT
• Title: How to Train Your Agent to Read and Write
• Authors: Li Liu, Mengge He, Guanghui Xu, Mingkui Tan, Qi Wu
• Abstract summary: Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. We propose a Deep ReAder-Writer (DRAW) network, which consists of a textitReader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text textitWriter that generates a novel paragraph, and a textit
• Score: 52.24605794920856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (\eg{students}) to fully {grasp} this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. Although there have been existing works focusing on summarizing (\emph{i.e.}, reading) the knowledge in a given text or generating (\emph{i.e.}, writing) a text based on the given knowledge, the ability of simultaneously reading and writing is still under development. Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice. In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a \textit{Reader} that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text \textit{Writer} that generates a novel paragraph, and a \textit{Reviewer} that reviews the generated paragraph from three different aspects. Extensive experiments show that our DRAW network outperforms considered baselines and several state-of-the-art methods on AGENDA and M-AGENDA datasets. Our code and supplementary are released at https://github.com/menggehe/DRAW.

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