We introduce the task of scene-aware dialog. Our goal is to generate a
complete and natural response to a question about a scene, given video and
audio of the scene and the history of previous turns in the dialog. To answer
successfully, agents must ground concepts from the question in the video while
leveraging contextual cues from the dialog history. To benchmark this task, we
introduce the Audio Visual Scene-Aware Dialog (AVSD) Dataset. For each of more
than 11,000 videos of human actions from the Charades dataset, our dataset
contains a dialog about the video, plus a final summary of the video by one of
the dialog participants. We train several baseline systems for this task and
evaluate the performance of the trained models using both qualitative and
quantitative metrics. Our results indicate that models must utilize all the
available inputs (video, audio, question, and dialog history) to perform best
on this dataset.

本论文介绍了场景感知对话任务，通过视频和音频研究场景，并在对话历史中利用上下文线索，以回答关于场景的问题；同时提出了 AVSD 数据集，并通过多项定量和定性指标评估了基础模型的表现，结果表明模型必须充分利用所有可用输入（视频、音频、问题和对话历史）才能在该数据集上取得最佳表现。