Layer-Wise Cross-View Decoding for Sequence-to-Sequence Learning
Table 5: Results of text summarization and image captioning. The † is defined similarly.
['Methods', 'Abstractive Summarization ROUGE-1', 'Abstractive Summarization ROUGE-2', 'Abstractive Summarization ROUGE-LCS', 'Image Captioning SPICE', 'Image Captioning CIDEr']
As we can see, the proposal achieves an advantage over the Transformer, which indicates that our approach generalizes well to tasks with much longer source sequences, which are around 400 words and is effective in dealing with longer sequences. In fact, in such scenarios, where the summary should be concise but also accurate in detail, the GCA naturally streamlines the decoding process. Image Captioning This task combines image understanding and language generation and is a cross-modal task compared to machine translation and text summarization. The source sequence contains non-ordered region-of-interest features Anderson et al. et al. CIDEr Vedantam et al. As we can see, our approach further improves the performance of baseline to 22.6 SPICE score and 129.4 CIDEr score, which is competitive with the state-of-the-art model AoANet Huang et al. The results suggest that the proposal could be extended to a wide range of sequence generation tasks with various kinds of source representations.