Narrative Theory for Computational Narrative Understanding
Narrative Theory for Computational Narrative Understanding
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DOI：
10.18653/v1/2021.emnlp-main.26
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Andrew Piper;R. So;David Bamman
中科院分区：
文献类型：
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作者：
Andrew Piper;R. So;David Bamman
在过去的十年中，自然语言处理领域已经开发了一系列用于推理叙事的计算方法，包括摘要，常识推理和事件检测。虽然这项工作带来了一个重要的经验透镜审查叙事，它是从大部分脱离的理论工作，在人文，社会和认知科学的叙事。在这份立场文件中，我们介绍了主导的理论框架的NLP社区，在不同的叙事学传统的NLP当前的研究，并认为，连接计算工作的NLP理论开辟了一系列新的经验问题，既有助于推进我们的理解叙事和开辟新的实际应用。
Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an important empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.