  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%.
