论文标题

蓝色亚马逊大脑(BLAB):关于巴西海事领土的模块化架构

The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory

论文作者

Pirozelli, Paulo, Castro, Ais B. R., de Oliveira, Ana Luiza C., Oliveira, André S., Cação, Flávio N., Silveira, Igor C., Campos, João G. M., Motheo, Laura C., Figueiredo, Leticia F., Pellicer, Lucas F. A. O., José, Marcelo A., José, Marcos M., Ligabue, Pedro de M., Grava, Ricardo S., Tavares, Rodrigo M., Matos, Vinícius B., Sym, Yan V., Costa, Anna H. R., Brandão, Anarosa A. F., Mauá, Denis D., Cozman, Fabio G., Peres, Sarajane M.

论文摘要

我们描述了针对巴西海事领土的人工特工开发的第一步,这是南大西洋境内的大型地区,也称为蓝色亚马逊。 “ Blue Amazon Brain”(BLAB)整合了许多服务,旨在传播有关该地区的信息及其重要性,并充当环境意识的工具。 BLAB提供的主要服务是一个对话设施,涉及有关Blue Amazon的复杂问题,称为Blab-Chat;它的中心组件是管理几个面向任务的自然语言处理模块(例如,问答和摘要系统)的控制器。这些模块可以访问内部数据湖以及第三方数据库。新闻记者(Blab-Reporter)和故意开发的Wiki(Blab-Wiki)也是Blab Service Architecture的一部分。在本文中,我们描述了当前版本的BLAB架构(接口,后端,Web服务,NLP模块和资源),并评论到目前为止我们所面临的挑战,例如缺乏培训数据和域名散布状态。解决这些问题在技术领域的人工智能开发中提出了巨大的挑战。

We describe the first steps in the development of an artificial agent focused on the Brazilian maritime territory, a large region within the South Atlantic also known as the Blue Amazon. The "BLue Amazon Brain" (BLAB) integrates a number of services aimed at disseminating information about this region and its importance, functioning as a tool for environmental awareness. The main service provided by BLAB is a conversational facility that deals with complex questions about the Blue Amazon, called BLAB-Chat; its central component is a controller that manages several task-oriented natural language processing modules (e.g., question answering and summarizer systems). These modules have access to an internal data lake as well as to third-party databases. A news reporter (BLAB-Reporter) and a purposely-developed wiki (BLAB-Wiki) are also part of the BLAB service architecture. In this paper, we describe our current version of BLAB's architecture (interface, backend, web services, NLP modules, and resources) and comment on the challenges we have faced so far, such as the lack of training data and the scattered state of domain information. Solving these issues presents a considerable challenge in the development of artificial intelligence for technical domains.

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