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Cross-Lingual Speaker Identification from Weak Local Evidence

Speaker identification, determining which character said each utterance in 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 our final model outperforms the previous state-of-the-art methods on two English speaker identification benchmarks by $5.4\%$ in accuracy, as well as two Chinese speaker identification datasets by up to $4.7\%$.

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