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• W3208482682 abstract "We devise a multimodal conversation system for dialogue utterances composed of text, image or both modalities. We leverage Auxiliary UnsuperviseD vIsual and TExtual Data (AUDITED). To improve the performance of text-based task, we utilize translations of target sentences from English to French to form the assisted supervision. For the image-based task, we employ the DeepFashion dataset in which we seek nearest neighbor images of positive and negative target images of the MMD data. These nearest neighbors form the nearest neighbor embedding providing an external context for target images. We form two methods to create neighbor embedding vectors, namely Neighbor Embedding by Hard Assignment (NEHA) and Neighbor Embedding by Soft Assignment (NESA) which generate context subspaces per target image. Subsequently, these subspaces are learnt by our pipeline as a context for the target data. We also propose a discriminator which switches between the image- and text-based tasks. We show improvements over baselines on the large-scale Multimodal Dialogue Dataset (MMD) and SIMMC." @default.
• W3208482682 created "2021-11-08" @default.
• W3208482682 creator A5002212263 @default.
• W3208482682 creator A5043287647 @default.
• W3208482682 date "2021-10-22" @default.
• W3208482682 modified "2023-09-27" @default.
• W3208482682 title "Simple Dialogue System with AUDITED" @default.
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• W3208482682 hasPublicationYear "2021" @default.
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