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Socially competent robots should be equipped with the ability to perceive theworld that surrounds them and communicate about it in a human-like manner.Representative skills that exhibit such ability include generating imagedescriptions and visually grounded referring expressions. In the NLG community,these generation tasks are largely investigated in non-interactive andlanguage-only settings. However, in face-to-face interaction, humans oftendeploy multiple modalities to communicate, forming seamless integration ofnatural language, hand gestures and other modalities like sketches. To enablerobots to describe what they perceive with speech and sketches/gestures, wepropose to model the task of generating natural language together withfree-hand sketches/hand gestures to describe visual scenes and real lifeobjects, namely, visually-grounded multimodal description generation. In thispaper, we discuss the challenges and evaluation metrics of the task, and howthe task can benefit from progress recently made in the natural languageprocessing and computer vision realms, where related topics such as visuallygrounded NLG, distributional semantics, and photo-based sketch generation havebeen extensively studied.

Enabling Robots to Draw and Tell: Towards Visually Grounded Multimodal Description Generation