NAREOR: The Narrative Reordering Problem

By Varun Prashant Gangal, Steven Y. Feng, Eduard H. Hovy, Teruko Mitamura. 2021

27 citations recorded by the source corpus.

Many implicit inferences exist in text depending on how it is structured that can critically impact the text's interpretation and meaning. One such structural aspect present in text with chronology is the order of its presentation. For narratives or stories, this is known as the narrative order. Reordering a narrative can impact the temporal, causal, event-based, and other inferences readers draw from it, which in turn can have strong effects both on its interpretation and interestingness. In this paper, we propose and investigate the task of Narrative Reordering (NAREOR) which involves rewriting a given story in a different narrative order while preserving its plot. We present a dataset, NAREORC, with human rewritings of stories within ROCStories in non-linear orders, and conduct a detailed analysis of it. Further, we propose novel task-specific training methods with suitable evaluation metrics. We perform experiments on NAREORC using state-of-the-art models such as BART and T5 and conduct extensive automatic and human evaluations. We demonstrate that although our models can perform decently, NAREOR is a challenging task with potential for further exploration. We also investigate two applications of NAREOR: generation of more interesting variations of stories and serving as adversarial sets for temporal/event-related tasks, besides discussing other prospective ones, such as for pedagogical setups related to language skills like essay writing and applications to medicine involving clinical narratives.

Subfields: Natural Language Processing

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “Other approaches include syntactic or controlled paraphrasing (Iyyer et al., 2018; Kumar et al., 2020), document-level paraphrasing (Gangal et al., 2021), augmenting misclassiﬁed examples (Dreossi et al., 2018), BERT cross-encoder labeling of new inputs (Thakur et al., 2021), guided generation…”
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021, background): “The idea to sample one or more additional sentences was first introduced by Jia and Liang (2017a).”
A Survey on Knowledge Distillation of Large Language Models (2024, background): “Unlike traditional DA techniques such as paraphrasing (Gangal et al., 2022) or back-translation (Longpre et al., 2019), which primarily aim at expanding the training dataset in a somewhat mechanical manner.”