Guided Generation of Cause and Effect Zhongyang Li1,2∗, Xiao Ding1, Ting Liu1, J. Edward Hu2 and Benjamin Van Durme2 1Harbin Institute of Technology, China 2Johns Hopkins University, USA fzyli,xding,
[email protected], fedward.hu,
[email protected] Abstract because they are hungry because they are lonely We present a conditional text generation frame- because they are in pain work that posits sentential expressions of possible cause because they want to be loved causes and effects. This framework depends on because they want to go home … two novel resources we develop in the course of babies cry this work: a very large-scale collection of English will lead to sleep problems sentences expressing causal patterns (CausalBank); can lead to depression and a refinement over previous work on construct- effect can lead to a bad marriage ing large lexical causal knowledge graphs (Cause can lead to bad food habits Effect Graph). Further, we extend prior work in result in tears to the eyes lexically-constrained decoding to support disjunc- … tive positive constraints. Human assessment con- Figure 1: Possible causes and effects generated by our model, con- firms that our approach gives high-quality and di- ditioned on the input sentence “babies cry”. Tokens in blue are verse outputs. Finally, we use CausalBank to per- constraint keywords derived from our Cause Effect Graph, which are form continued training of an encoder supporting forced to be included in the outputs by constrained decoding. a recent state-of-the-art model for causal reason- ing, leading to a 3-point improvement on the COPA tures causal relations as a mapping across lexical types, lemma- challenge set, with no change in model architecture.