Conditional set generation using Seq2seq models. (arXiv:2205.12485v1 [cs.CL])

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. Sequence-to-sequence~(Seq2seq) models
are a popular choice to model set generation, but they 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. Further, we …

arxiv generation seq2seq set