论文标题
使用SEQ2SEQ模型的有条件设置生成
Conditional set generation using Seq2seq models
论文作者
论文摘要
有条件的设置生成从代币的输入序列到集合学习了映射。几个NLP任务，例如实体键入和对话情感标记，是设定生成的实例。 Seq2Seq模型是设置生成的流行选择，将集合视为序列，并且不能完全利用其关键属性，即订单不变性和基数。我们提出了一种新型算法，用于在标签订单的组合空间上有效采样信息订单。我们通过预先准备设定的大小并利用SEQ2SEQ模型使用的自回归分解来共同对设定的基数和输出进行建模。我们的方法是一种独立于模型的数据增强方法，该方法赋予了任何具有订单不变和基数信号的SEQ2SEQ模型。培训有关此增强数据的SEQ2SEQ模型（无需任何其他注释）在各种模型的四个基准数据集上的平均相对相对提高为20％：BART，T5和GPT-3。使用setaug的代码可用：https：//setgen.structgen.com。
Conditional set generation learns a mapping from an input sequence of tokens to a set. Several NLP tasks, such as entity typing and dialogue emotion tagging, are instances of set generation. Seq2Seq models, a popular choice for set generation, treat a set as a sequence and do not fully leverage its key properties, namely order-invariance and cardinality. We propose a novel algorithm for effectively sampling informative orders over the combinatorial space of label orders. We jointly model the set cardinality and output by prepending the set size and taking advantage of the autoregressive factorization used by Seq2Seq models. Our method is a model-independent data augmentation approach that endows any Seq2Seq model with the signals of order-invariance and cardinality. Training a Seq2Seq model on this augmented data (without any additional annotations) gets an average relative improvement of 20% on four benchmark datasets across various models: BART, T5, and GPT-3. Code to use SETAUG available at: https://setgen.structgen.com.
