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• W2891997820 abstract "Despite being virtually ubiquitous, sequence-to-sequence models are challenged by their lack of diversity and inability to be externally controlled. In this paper, we speculate that a fundamental shortcoming of sequence generation models is that the decoding is done strictly from left-to-right, meaning that outputs values generated earlier have a profound effect on those generated later. To address this issue, we propose a novel middle-out decoder architecture that begins from an initial middle-word and simultaneously expands the sequence in both directions. To facilitate information flow and maintain consistent decoding, we introduce a dual self-attention mechanism that allows us to model complex dependencies between the outputs. We illustrate the performance of our model on the task of video captioning, as well as a synthetic sequence de-noising task. Our middle-out decoder achieves significant improvements on de-noising and competitive performance in the task of video captioning, while quantifiably improving the caption diversity. Furthermore, we perform a qualitative analysis that demonstrates our ability to effectively control the generation process of our decoder." @default.
• W2891997820 created "2018-09-27" @default.
• W2891997820 creator A5014641065 @default.
• W2891997820 creator A5053011888 @default.
• W2891997820 date "2018-10-28" @default.
• W2891997820 modified "2023-09-27" @default.
• W2891997820 title "Middle-Out Decoding" @default.
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• W2891997820 hasPublicationYear "2018" @default.
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