Narrative reasoning relies on the understanding of eventualities in story
contexts, which requires a wealth of background world knowledge. To help
machines leverage such knowledge, existing solutions can be categorized into
two groups. Some focus on implicitly modeling eventuality knowledge by
pretraining language models (LMs) with eventuality-aware objectives. However,
this approach breaks down knowledge structures and lacks interpretability.
Others explicitly collect world knowledge of eventualities into structured
eventuality-centric knowledge graphs (KGs). However, existing research on
leveraging these knowledge sources for free-texts is limited. In this work, we
propose an initial comprehensive framework called EventGround, which aims to
tackle the problem of grounding free-texts to eventuality-centric KGs for
contextualized narrative reasoning. We identify two critical problems in this
direction: the event representation and sparsity problems. We provide simple
yet effective parsing and partial information extraction methods to tackle
these problems. Experimental results demonstrate that our approach consistently
outperforms baseline models when combined with graph neural network (GNN) or
large language model (LLM) based graph reasoning models. Our framework,
incorporating grounded knowledge, achieves state-of-the-art performance while
providing interpretable evidence.

通过将自由文本与事件中心知识图关联，本研究提出了一个综合框架 EventGround，旨在解决上下文化叙事推理的问题，并通过基于图神经网络和大型语言模型的图推理模型，在取得了最新成果的同时提供可解释性证据。

EventGround: 基于事件为中心的知识图谱的叙事推理

EventGround: Narrative Reasoning by Grounding to Eventuality-centric  Knowledge Graphs

Narrative, and in particular storytelling, is an important part of the human
experience. Consequently, computational systems that can reason about narrative
can be more effective communicators, entertainers, educators, and trainers. One
of the central challenges in computational narrative reasoning is narrative
generation, the automated creation of meaningful event sequences. There are
many factors -- logical and aesthetic -- that contribute to the success of a
narrative artifact. Central to this success is its understandability. We argue
that the following two attributes of narratives are universal: (a) the logical
causal progression of plot, and (b) character believability. Character
believability is the perception by the audience that the actions performed by
characters do not negatively impact the audiences suspension of disbelief.
Specifically, characters must be perceived by the audience to be intentional
agents. In this article, we explore the use of refinement search as a technique
for solving the narrative generation problem -- to find a sound and believable
sequence of character actions that transforms an initial world state into a
world state in which goal propositions hold. We describe a novel refinement
search planning algorithm -- the Intent-based Partial Order Causal Link (IPOCL)
planner -- that, in addition to creating causally sound plot progression,
reasons about character intentionality by identifying possible character goals
that explain their actions and creating plan structures that explain why those
characters commit to their goals. We present the results of an empirical
evaluation that demonstrates that narrative plans generated by the IPOCL
algorithm support audience comprehension of character intentions better than
plans generated by conventional partial-order planners.

该研究探讨了使用细分搜索解决情节生成问题的可能性，并提出了一种新型的细分搜索规划算法，可以创建具有因果关系的情节进展，并在识别可能的角色目标和创建计划结构方面解释为什么这些角色承诺他们的目标。结果表明，IPOCL 算法生成的叙述计划比传统的部分顺序规划器生成的计划更能支持观众理解角色意图。