In this paper, we argue that the design and development of multimodal datasets for natural language processing (NLP) challenges should be enhanced in two significant respects: to more broadly represent commonsense semantic inferences; and to better reflect the dynamics of actions and events, through a substantive alignment of textual and visual information. We identify challenges and tasks that are reflective of linguistic and cognitive competencies that humans have when speaking and reasoning, rather than merely the performance of systems on isolated tasks. We introduce the distinction between challenge-based tasks and competence-based performance, and describe a diagnostic dataset, Recipe-to-Video Questions (R2VQ), designed for testing competence-based comprehension over a multimodal recipe collection (http://r2vq.org/). The corpus contains detailed annotation supporting such inferencing tasks and facilitating a rich set of question families that we use to evaluate NLP systems.
We describe an ongoing project in learning to perform primitive actions from demonstrations using an interactive interface. In our previous work, we have used demonstrations captured from humans performing actions as training samples for a neural network-based trajectory model of actions to be performed by a computational agent in novel setups. We found that our original framework had some limitations that we hope to overcome by incorporating communication between the human and the computational agent, using the interaction between them to fine-tune the model learned by the machine. We propose a framework that uses multimodal human-computer interaction to teach action concepts to machines, making use of both live demonstration and communication through natural language, as two distinct teaching modalities, while requiring few training samples.
* Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)