The task of Visual Dialogue involves a conversation between an agent system and a human end-user regarding visual information presented. The conversation consists of multiple rounds of questions and answers. The key challenge for the agent system is to answer the questions of human users with meaningful information while keeping the conversation flow contiguous and natural. The current visual dialogue systems can be divided into two tracks, generative models and discriminative models. The discriminative models cannot directly generate a response but choose a response out of a pool of candidate responses. Although the discriminative models have achieved surprising results, they are usually not applicable in real scenarios where no candidate response pool is available. On the other hand, the generative models can directly generate a response based on the input information. However, most generative models based on maximum likelihood estimation (MLE) approach suffer from the tendency of generating generic responses.
We present a novel approach that incorporates a multi-modal recurrently guided attention mechanism with a simple yet effective training scheme to generate high quality responses in the Visual Dialogue interaction model. Our attention mechanism combines attentions globally from multiple modalities (e.g., image, text questions and dialogue history), and refines them locally and simultaneously for each modality. Generators using typical MLE-based methods only learn from good answers, and consequently tend to generate safe or generic responses. The new training scheme with weighted likelihood estimation (WLE) penalizes generic responses for unpaired questions in the training and enables the generator to learn from poor answers as well.
On benchmark dataset, our proposed Visual Dialogue system demonstrates state-of-the-art performance with improvement of 5.81% and 5.28 on recall@10 and mean rank, respectively.
–By Heming Zhang
