论文标题
COD3S：具有离散语义签名的多元化一代
COD3S: Diverse Generation with Discrete Semantic Signatures
论文作者
论文摘要
我们提出COD3S，这是一种使用神经序列到序列（SEQ2SEQ）模型生成语义上不同句子的新方法。 SEQ2SEQ模型以输入为条件，通常会在语义和句法上产生一组句子集，从而在一到一对序列的生成任务上表现不佳。我们的两阶段方法通过对基于局部敏感的哈希（LSH）的语义句子代码进行调节来提高输出多样性，其锤击距离与人类对语义文本相似性的判断高度相关。尽管通常适用，但我们将COD3应用于因果生成，这是预测命题的合理原因或效果的任务。我们通过自动和人类评估证明，使用我们的方法产生的响应表现出改善的多样性，而不会降低任务绩效。
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.
