COD3S: Diverse Generation with Discrete Semantic Signatures
Nathaniel Weir, João Sedoc, Benjamin Van Durme · 2020
We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models.Conditioned on an input, seq2seq models typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks.Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity.Though it is generally applicable, we apply COD3S to causal generation, the task of predicting a proposition's plausible causes or effects.We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.