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Counterfactual Story Reasoning and Generation (1909.04076v2)

Published 9 Sep 2019 in cs.CL and cs.AI

Abstract: Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes. Despite being considered a 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 80,115 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 LLMs, and assess whether common overlap and model-based automatic metrics for text generation correlate well with human scores for counterfactual rewriting.

Citations (134)

Summary

  • The paper introduces the counterfactual story rewriting task using a new dataset of 29,849 stories with human-generated revisions.
  • It evaluates pretrained baseline models (GPT, GPT-2) in zero-shot, unsupervised, and supervised settings, revealing challenges in maintaining narrative coherence.
  • Human evaluations highlight that traditional metrics like BLEU and ROUGE poorly capture counterfactual alignment, underscoring the need for enhanced reasoning in AI storytelling.

Overview of "Counterfactual Story Reasoning and Generation" Paper

The paper entitled "Counterfactual Story Reasoning and Generation" introduces a novel task for NLP systems, namely Counterfactual Story Rewriting. This task entails generating a revised version of a story that incorporates an alternative event, resulting in a narrative that remains consistent with altered scenarios. Despite the broad recognition of counterfactual reasoning as a key component in AI-complete systems, prior studies have provided limited resources for assessing such reasoning within the context of narratives. The researchers present a specialized dataset and various baseline model assessments to further explore this domain.

Dataset and Task Design

The dataset introduced by the authors, named the Counterfactual Story Rewriting dataset, consists of 29,849 rewritten stories. Each entry in the dataset includes the original short story along with a counterfactual event—a hypothetical alternative to a specific story event—and the human-generated revision that maintains consistency with this counterfactual change. Additionally, the dataset comprises 80,115 counterfactual events without corresponding revised stories, serving as a resource for potential future work on unsupervised approaches.

The crux of the task is to modify the original narrative minimally to fit the newly introduced counterfactual condition. Thus, it obliges a deeper understanding of causal narrative structures and counterfactual invariances from NLP models. The task not only demands narrative coherence and plausibility but also minimal deviation from the source narrative in non-altered segments.

Model Evaluations

The paper assesses several competitive baseline models, primarily focusing on pretrained LLMs, including GPT, GPT-2 Small, and GPT-2 Medium under various training paradigms—zero-shot, unsupervised fine-tuning, and supervised learning. The experimental results reveal that while existing models have achieved notable success in certain aspects of natural language generation, they significantly struggle in maintaining full narrative consistency when integrating counterfactual reasoning.

Human Evaluation and Metric Correlation

A detailed evaluation by human judges further investigates the quality of model-generated outputs against human-written reference revisions. The paper finds that current automatic metrics like BLEU and ROUGE, while decent at capturing structural similarity to reference texts, showed inadequate correlation with human judgments regarding adherence to counterfactual conditions. On the other hand, model-based metrics like BERTScore provided positive correlations across a broader range of criteria, though improvements are needed for more nuanced evaluations like counterfactual alignment.

Implications and Future Directions

The paper underlines the potential for utilizing counterfactual reasoning tasks to explore and advance narrative understanding capabilities in AI systems. Furthermore, it suggests that neural LLMs, despite their prowess in other domains of text generation, require foundational improvements in reasoning abilities. The provision of novel datasets and the benchmarks set by the paper pave the way for future research into enhancing causal reasoning in AI, thereby fostering the development of more sophisticated and coherent narrative generation models.

In summary, the paper highlights a new challenging task that remains underexplored within NLP and sets a substantive foundation for integrating reasoning capabilities within LLMs. Future research could build on these insights to extend counterfactual reasoning frameworks and refine generative capabilities languaging thereby addressing broader narrative coherence challenges across varied fictional and real-world contexts.