Conditional Text Generation for Harmonious Human-Machine Interaction

Machine Comprehension by Text-to-Text Neural Question Generation

We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most no...

Human-machine Interaction

Image Caption Generation with Text-Conditional Semantic Attention

Attention mechanisms have attracted considerable interest in image captioning due to its powerful performance. However, existing methods use only visual content as attention and whether textual context can improve attention in image captioning remains unsolved. To explore this problem, we propose a novel attention mechanism, called textconditional attention, which allows the caption generator t...

2D Audiovisual Text-to-Speech Synthesis for Human-Machine Interaction in Dutch

Speech has always been the most important means of communication between humans. Therefore, using speech in machine-human communication can help in increasing the naturalness of the communication between a computer system and a user. Systems that can make a machine pronounce any given input text are referred to as text-to-speech systems. To further enhance the communication, a talking head can ...

Affective communication for implicit human-machine interaction

∗ 0-7803-7952-7/03/$17.00 © IEEE 2003 Abstract: A novel implicit communication framework in human-machine interaction that is sensitive to human affective states is presented in this paper. The focus is to achieve detection and recognition of human affect based on physiological signals. This involves building an affect recognition system that accepts as input various physiological parameters an...