Counterfactual Story Reasoning and Generation

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 5043–5053, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 5043 Counterfactual Story Reasoning and Generation Lianhui Qin ♠♦ Antoine Bosselut ♠♦ Ari Holtzman ♠♦ Chandra Bhagavatula ♦ Elizabeth Clark ♠ Yejin Choi ♠♦ ♠Paul G. Allen School of Computer Science & Engineering, University of Washington ♦Allen Institute for Artificial Intelligence {lianhuiq,antoineb,ahai,eaclark7,yejin}@cs.washington.edu chandrab@allenai.org Abstract Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in adifferent outcomes. Despite being considered necessary component of AI-complete systems, few resources have been developed for evaluating counterfactual reasoning in narratives. In this paper, we propose Counterfactual Story Rewriting: given an original story and an intervening counterfactual event, the task is to minimally revise the story to make it compatible with the given counterfactual event. Solving this task will require deep understanding of causal narrative chains and counterfactual invariance, and integration of such story reasoning capabilities into conditional language generation models. We present TIMETRAVEL, a new dataset of 29,849 counterfactual rewritings, each with the original story, a counterfactual event, and human-generated revision of the original story compatible with the counterfactual event. Additionally, we include 81,407 counterfactual “branches” without a rewritten storyline to support future work on semi- or un-supervised approaches to counterfactual story rewriting. Finally, we evaluate the counterfactual rewriting capacities of several competitive baselines based on pretrained language models, and assess whether common overlap and model-based automatic metrics for text generation correlate well with human scores for counterfactual rewriting. 1 Introduction A desired property of AI systems is counterfactual reasoning: the ability to predict causal changes in future events given a counterfactual condition He decided to be a vampire this year. Pierre loved Halloween. He got a black cape and white face paint. His fake teeth were uncomfortable but looked great. Pierre couldn’t wait to go trick or treating! He decided to be a werewolf this year. He got a brown sweater and matching face mask. His mask was uncomfortable but looked great. Pierre couldn’t wait to go trick or treating! If “he decided to be a werewolf” instead of a vampire, he will need a different costume. If he used a mask instead of fake teeth, the uncomfortable thing would have been the mask. Ending is still valid in the counterfactual scenario. What if vampire —> werewolf Figure 1: Given a short story (left column) and a counterfactual context (“He decided to be a werewolf this year”), the task is to revise the original story with minimal edits to be consistent with both the original premise (“Pierre loved Halloween”) and the new counterfactual situation. The modified parts in the new story (right column) are highlighted in red. applied to the original chain of events (Goodman, 1947; Bottou et al., 2013). For example, given an original story shown in the left chain in Figure 1, where “Pierre loved Halloween. He decided to be a …

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