Big Bidirectional Insertion Representations for Documents
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The Insertion Transformer is well suited for long form text generation due to its parallel generation capabilities, requiring O(log_2 n) generation steps to generate n tokens. However, modeling long sequences is difficult, as there is more ambiguity captured in the attention mechanism. This work proposes the Big Bidirectional Insertion Representations for Documents (Big BIRD), an insertion-based model for document-level translation tasks. We scale up the insertion-based models to long form documents. Our key contribution is introducing sentence alignment via sentence-positional embeddings between the source and target document. We show an improvement of +4.3 BLEU on the WMT'19 English→German document-level translation task compared with the Insertion Transformer baseline.
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