利用虚构人物嵌入改进引语归因
Improving Quotation Attribution with Fictional Character Embeddings
AI总结：
针对文学作品中回指和隐式引语归因困难的问题，提出将基于文体模型的全局人物嵌入融入BookNLP系统，并构建DramaCV语料库，在28部小说上达到最先进性能。
摘要：
人类在文学作品中自然地会将直接引语的话语归因于其说话者。在归因引语时，我们处理上下文信息，同时也会获取我们在整个叙事过程中构建和修正的人物心理表征。最近，自动归因此类话语的方法探索了在处理上下文信息时，用确定性规则模拟人类逻辑，或用神经网络学习新的隐式规则。然而，这些系统本质上缺乏人物表征，这常常导致在更具挑战性的归因示例（如回指和隐式引语）中出现错误。在这项工作中，我们提出用人物嵌入来增强一个流行的引语归因系统BookNLP，这些嵌入编码了源自现成文体测量模型——通用作者身份表征（UAR）的人物全局文体信息。我们创建了DramaCV（代码和数据可在https://github.com/deezer/character_embeddings_qa 获取），这是一个包含15至20世纪英文戏剧剧本的语料库，我们自动标注了虚构人物话语的作者身份验证，并发布了两个在DramaCV上训练的、专为文学人物分析定制的UAR版本。然后，通过对28部小说的广泛评估，我们表明将BookNLP的上下文信息与我们提出的全局人物嵌入相结合，可以提高回指和隐式引语说话者识别的准确性，达到最先进的性能。
英文摘要：
Humans naturally attribute utterances of direct speech to their speaker in literary works. When attributing quotes, we process contextual information but also access mental representations of characters that we build and revise throughout the narrative. Recent methods to automatically attribute such utterances have explored simulating human logic with deterministic rules or learning new implicit rules with neural networks when processing contextual information. However, these systems inherently lack \textit{character} representations, which often leads to errors in more challenging examples of attribution: anaphoric and implicit quotes. In this work, we propose to augment a popular quotation attribution system, BookNLP, with character embeddings that encode global stylistic information of characters derived from an off-the-shelf stylometric model, Universal Authorship Representation (UAR). We create DramaCV (Code and data can be found at https://github.com/deezer/character_embeddings_qa ), a corpus of English drama plays from the 15th to 20th century that we automatically annotate for Authorship Verification of fictional characters utterances, and release two versions of UAR trained on DramaCV, that are tailored for literary characters analysis. Then, through an extensive evaluation on 28 novels, we show that combining BookNLP's contextual information with our proposed global character embeddings improves the identification of speakers for anaphoric and implicit quotes, reaching state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
