Guided Generation of Cause and Effect

Understand

We present a conditional text generation framework that posits sentential expressions of possible causes and effects.

• This framework depends on two novel resources we develop in the course of this work: a very large-scale collection of English sentences expressing causal patterns CausalBank; and a refinement over previous work on constructing large lexical causal knowledge graphs Cause Effect Graph.
• Further, we extend prior work in lexically-constrained decoding to support disjunctive positive constraints.
• Human assessment confirms that our approach gives high-quality and diverse outputs.