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Register a new userNarrative Theory for Computational Narrative Understanding
النظرية السردية لفهم السرد الحسابي
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على مدار العقد الماضي، طورت مجال معالجة اللغة الطبيعية مجموعة واسعة من الأساليب الحسابية لمعرفة الرواية، بما في ذلك تلخيص، استنتاج المنطقي، والكشف عن الحدث.في حين أن هذا العمل قد جلب عدسة تجريبية مهمة لفحص السرد، فهو مطلقات كبيرة من الجسم الكبير من العمل النظري على السرد داخل العلوم الإنسانية والاجتماعية والعلوم المعرفية.في هذه الورقة الموضعية، نقدم الأطر النظرية المهيمنة إلى مجتمع NLP، وتوليد البحوث الحالية في NLP داخل التقاليد السريعة المميزة، وتجادل أن ربط العمل الحسابي في NLP بالنظرية يفتح مجموعة من الأسئلة التجريبية الجديدة التي من شأنها أن تساعد كلاهما في التقدم لدينافهم السرد وفتح تطبيقات عملية جديدة.
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.
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When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the
narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
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Narrative analysis is becoming increasingly important for a number of linguistic tasks including summarization, knowledge extraction, and question answering. We present a novel approach for narrative event representation using attention to re-context
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Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process. It is desirable to extract the event chains that repres
ent such processes. However, this extraction remains a challenging problem. We posit that this is due to the nature of the texts from which chains are discovered. Natural language text interleaves a narrative of concrete, salient events with background information, contextualization, opinion, and other elements that are important for a variety of necessary discourse and pragmatics acts but are not part of the principal chain of events being communicated. We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. We demonstrate the effectiveness of our methods at isolating critical event chains by comparing their effect on downstream tasks. We show that by pre-training large language models on our extracted chains, we obtain improvements in two tasks that benefit from a clear understanding of event chains: narrative prediction and event-based temporal question answering. The demonstrated improvements and ablative studies confirm that our extraction method isolates critical event chains.
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