Incorporating Circumstances into Narrative Event Prediction

Wang, Qianqian; Cai, Xiangrui; Wang, Hongbin; Yuan, Xiaojie

doi:10.18653/v1/2021.findings-emnlp.416

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…NarGNN [32] integrates factual knowledge and experiential knowledge to predict events and considers both historical and relative information. CircEvent [28] retrieves circumstances at both local and global levels using multi-head attention, considering not only inter-event relationships but also how the events happened.…”

Section: Methodsmentioning

confidence: 99%

Script event prediction by meta-memory low resource network

Chu

Hou

Weng

et al. 2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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Script event prediction involves building a model using recent event sequence records to capture the correlation between events and predict future ones. However, existing methods require a large amount of training data to perform well. In many real-world scenarios, labeled data is scarce and data distribution is uneven, making it challenging to achieve satisfactory performance. Additionally, the learning process of many models demands significant computational resources, which can render them unsuitable for low-resource settings. In order to fill the task blank of event prediction in low resource scenarios, a general framework called Meta-memory low resource network learning model(MLN) is proposed. Firstly, MLN captures the memory of events that occurred in the history through trend learner, learns the context-implied representation between events, and can predict future events based on the implied representation. Secondly, a Meta-memory predictor is proposed, which can further aggregate similar contextual implicit representations to form meta-memory representations, To realize the long-term memory learning process under the condition of few-shot. Finally, a gated network combination method combines the trend learner with the Meta-memory predictor to improve the effect of event prediction. We conducted experiments on four real data sets, and the results showed the effectiveness of MLN.

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Section: Methodsmentioning

confidence: 99%

Script event prediction by meta-memory low resource network

Chu

Hou

Weng

et al. 2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

View full text Add to dashboard Cite

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“…The first is script event prediction, which is to predict the end event of a given event chain. It includes the early script event prediction method based on statistical information (Chambers and Jurafsky 2008;Jans et al 2012;Peyré, Cuturi et al 2019), and the recent script event prediction method based on neural network (Pichotta and Mooney 2016;Granroth-Wilding and Clark 2016;Lv, Zhu, and Hu 2020;Bai et al 2021;Zhou et al 2021;Wang et al 2021). The second line of work on script events is script event generation.…”

Section: Script Event Learningmentioning

confidence: 99%

Event Process Typing via Hierarchical Optimal Transport

Zhang

Chen

Liu

et al. 2023

AAAI

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Understanding intention behind event processes in texts is important to many applications. One challenging task in this line is event process typing, which aims to tag the process with one action label and one object label describing the overall action of the process and object the process likely affects respectively. To tackle this task, existing methods mainly rely on the matching of the event process level and label level representation, which ignores two important characteristics: Process Hierarchy and Label Hierarchy. In this paper, we propose a Hierarchical Optimal Transport (HOT) method to address the above problem. Specifically, we first explicitly extract the process hierarchy and label hierarchy. Then the HOT optimally matches the two types of hierarchy. Experimental results show that our model outperforms the baseline models, illustrating the effectiveness of our model.

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“…Narrative Consistency Check involves determining whether an assertion aligns with the narrative or contradicts it. This task encompasses various scopes, ranging from the entire narrative structure (Ouyang and McKeown, 2014) to discourse structure (Mihaylov and Frank, 2019) and ultimately, the constituents of the narrative, such as agents and events (Piper et al, 2021;Wang et al, 2021).…”

Section: Narrative Reading Comprehensionmentioning

confidence: 99%

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Zhu,

Zhao,

Gui

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author's imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension.

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Incorporating Circumstances into Narrative Event Prediction

Cited by 6 publications

References 27 publications

Script event prediction by meta-memory low resource network

Script event prediction by meta-memory low resource network

Event Process Typing via Hierarchical Optimal Transport

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

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