Spurthi Sandiri


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Multimodal Context Carryover
Prashan Wanigasekara | Nalin Gupta | Fan Yang | Emre Barut | Zeynab Raeesy | Kechen Qin | Stephen Rawls | Xinyue Liu | Chengwei Su | Spurthi Sandiri
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Multi-modality support has become an integral part of creating a seamless user experience with modern voice assistants with smart displays. Users refer to images, video thumbnails, or the accompanying text descriptions on the screen through voice communication with AI powered devices. This raises the need to either augment existing commercial voice only dialogue systems with state-of-the-art multimodal components, or to introduce entirely new architectures; where the latter can lead to costly system revamps. To support the emerging visual navigation and visual product selection use cases, we propose to augment commercially deployed voice-only dialogue systems with additional multi-modal components. In this work, we present a novel yet pragmatic approach to expand an existing dialogue-based context carryover system (Chen et al., 2019a) in a voice assistant with state-of-the-art multimodal components to facilitate quick delivery of visual modality support with minimum changes. We demonstrate a 35% accuracy improvement over the existing system on an in-house multi-modal visual navigation data set.