Abstract
We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks. The system generates text predictions to different sequence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The system reacts to each correction, providing alternative hypotheses, compelling with the feedback provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client-server architecture. For accessing the system, we developed a website, which communicates with the neural model, hosted in a local server. From this website, the different tasks can be tackled following the interactive-predictive framework. We open-source all the code developed for building this system. The demonstration in hosted in this http URL.
Abstract (translated)
我们提出了一个神经交互预测系统的演示，以解决多模序列到序列的任务。系统生成不同顺序的文本预测，以排序任务：机器翻译、图像和视频字幕。这些预测是由一个以字符的形式引入修正的人工智能修改的。系统对每一个修正做出反应，提供替代假设，并通过用户提供的反馈进行强制。最终目标是减少纠正过程中所需的人力。这个系统是按照客户机-服务器体系结构实现的。为了访问这个系统，我们开发了一个网站，它与神经模型通信，托管在一个本地服务器上。从这个网站，可以按照交互式预测框架来处理不同的任务。我们对为构建这个系统而开发的所有代码进行了开源。此HTTP URL中承载的演示。
URL
https://arxiv.org/abs/1905.08181
