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Proceedings of the Third Workshop on Narrative Understanding最新文献

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Gender and Representation Bias in GPT-3 Generated Stories GPT-3生成故事中的性别和代表性偏见
Proceedings of the Third Workshop on Narrative Understanding Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.NUSE-1.5
Li Lucy, David Bamman
{"title":"Gender and Representation Bias in GPT-3 Generated Stories","authors":"Li Lucy, David Bamman","doi":"10.18653/V1/2021.NUSE-1.5","DOIUrl":"https://doi.org/10.18653/V1/2021.NUSE-1.5","url":null,"abstract":"Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and descriptions depending on GPT-3’s perceived gender of the character in a prompt, with feminine characters more likely to be associated with family and appearance, and described as less powerful than masculine characters, even when associated with high power verbs in a prompt. Our study raises questions on how one can avoid unintended social biases when using large language models for storytelling.","PeriodicalId":316373,"journal":{"name":"Proceedings of the Third Workshop on Narrative Understanding","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124567902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 145
FanfictionNLP: A Text Processing Pipeline for Fanfiction FanfictionNLP:同人小说的文本处理管道
Proceedings of the Third Workshop on Narrative Understanding Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.NUSE-1.2
Michael Miller Yoder, Sopan Khosla, Qinlan Shen, Aakanksha Naik, Huiming Jin, Hariharan Muralidharan, C. Rosé
{"title":"FanfictionNLP: A Text Processing Pipeline for Fanfiction","authors":"Michael Miller Yoder, Sopan Khosla, Qinlan Shen, Aakanksha Naik, Huiming Jin, Hariharan Muralidharan, C. Rosé","doi":"10.18653/V1/2021.NUSE-1.2","DOIUrl":"https://doi.org/10.18653/V1/2021.NUSE-1.2","url":null,"abstract":"Fanfiction presents an opportunity as a data source for research in NLP, education, and social science. However, answering specific research questions with this data is difficult, since fanfiction contains more diverse writing styles than formal fiction. We present a text processing pipeline for fanfiction, with a focus on identifying text associated with characters. The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters. Additionally, the pipeline contains a novel approach to character coreference that uses knowledge from quote attribution to resolve pronouns within quotes. For each module, we evaluate the effectiveness of various approaches on 10 annotated fanfiction stories. This pipeline outperforms tools developed for formal fiction on the tasks of character coreference and quote attribution","PeriodicalId":316373,"journal":{"name":"Proceedings of the Third Workshop on Narrative Understanding","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129389118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Towards a Model-Theoretic View of Narratives 论叙事的模式观
Proceedings of the Third Workshop on Narrative Understanding Pub Date : 2021-03-23 DOI: 10.18653/V1/2021.NUSE-1.10
Louis Castricato, Georgia Tech EleutherAI, Stella Rose Biderman, R. E. Cardona-Rivera
{"title":"Towards a Model-Theoretic View of Narratives","authors":"Louis Castricato, Georgia Tech EleutherAI, Stella Rose Biderman, R. E. Cardona-Rivera","doi":"10.18653/V1/2021.NUSE-1.10","DOIUrl":"https://doi.org/10.18653/V1/2021.NUSE-1.10","url":null,"abstract":"In this paper, we propose the beginnings of a formal framework for modeling narrative qua narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader’s story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader, along with two novel measurements of story coherence.","PeriodicalId":316373,"journal":{"name":"Proceedings of the Third Workshop on Narrative Understanding","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130408017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies 基于跨事件依赖关系的高效端到端学习的文档级事件提取
Proceedings of the Third Workshop on Narrative Understanding Pub Date : 2020-10-24 DOI: 10.18653/V1/2021.NUSE-1.4
Kung-Hsiang Huang, Nanyun Peng
{"title":"Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies","authors":"Kung-Hsiang Huang, Nanyun Peng","doi":"10.18653/V1/2021.NUSE-1.4","DOIUrl":"https://doi.org/10.18653/V1/2021.NUSE-1.4","url":null,"abstract":"Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.","PeriodicalId":316373,"journal":{"name":"Proceedings of the Third Workshop on Narrative Understanding","volume":"33 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120875120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 25
Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences 学习电影角色之间的相似性及其对理解人类经验的潜在意义
Proceedings of the Third Workshop on Narrative Understanding Pub Date : 2020-10-23 DOI: 10.18653/V1/2021.NUSE-1.3
Zhiling Wang, Weizhe Lin, Xiaodong Wu
{"title":"Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences","authors":"Zhiling Wang, Weizhe Lin, Xiaodong Wu","doi":"10.18653/V1/2021.NUSE-1.3","DOIUrl":"https://doi.org/10.18653/V1/2021.NUSE-1.3","url":null,"abstract":"While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness. We propose a new task to capture this richness based on an unlikely setting: movie characters. We sought to capture theme-level similarities between movie characters that were community-curated into 20,000 themes. By introducing a two-step approach that balances performance and efficiency, we managed to achieve 9-27% improvement over recent paragraph-embedding based methods. Finally, we demonstrate how the thematic information learnt from movie characters can potentially be used to understand themes in the experience of people, as indicated on Reddit posts.","PeriodicalId":316373,"journal":{"name":"Proceedings of the Third Workshop on Narrative Understanding","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127279483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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