论文标题
CopyNext：按顺序与序列模型进行显式跨度复制和对齐方式
CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models
论文作者
论文摘要
复制机制以序列为序列模型（SEQ2SEQ）来生成从输入到输出的单词的再生产。这些框架在词汇类型级别上运行，无法提供明确的对齐方式，该框架记录每个令牌从哪里复制。此外，它们需要单独复制输入（跨度）的连续令牌序列。我们提出了一个具有显式令牌级复制操作的模型，并将其扩展到复制整个跨度。我们的模型在输入和输出中提供了跨度的硬对齐，从而允许SEQ2SEQ的非传统应用，例如信息提取。我们演示了嵌套命名实体识别的方法，并以数量级的解码速度提高了几乎最先进的精度。
Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed.
