论文标题
使用远处监督的跨语言扬声器识别
Cross-Lingual Speaker Identification Using Distant Supervision
论文作者
论文摘要
说话者身份证明，确定哪个角色在文学文本中说的每种话语都受益于许多下游任务。大多数现有的方法都使用专家定义的规则或基于规则的功能直接处理此任务，但是这些方法具有重要的缺点，例如缺乏上下文推理和不良的跨语性概括。在这项工作中，我们提出了一个解决这些问题的演讲者身份识别框架。我们首先通过通用工具和启发式方法提取英文中的大规模远处监督信号，然后应用这些弱标记的实例，重点是鼓励上下文推理来培训跨语性语言模型。我们表明，最终的模型优于以前的两种英语识别基准的先前最先进方法的准确性高达9％，而仅在远处监督下进行5％，以及两个中文说话者识别数据集高达4.7％。
Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks. Most existing approaches use expert-defined rules or rule-based features to directly approach this task, but these approaches come with significant drawbacks, such as lack of contextual reasoning and poor cross-lingual generalization. In this work, we propose a speaker identification framework that addresses these issues. We first extract large-scale distant supervision signals in English via general-purpose tools and heuristics, and then apply these weakly-labeled instances with a focus on encouraging contextual reasoning to train a cross-lingual language model. We show that the resulting model outperforms previous state-of-the-art methods on two English speaker identification benchmarks by up to 9% in accuracy and 5% with only distant supervision, as well as two Chinese speaker identification datasets by up to 4.7%.
