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arXiv · 2210.05780

Cross-Lingual Speaker Identification Using Distant Supervision

Ben Zhou·Dian Yu·Dong Yu·Dan Roth
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Abstract

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%.

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Ben Zhou, Dian Yu, Dong Yu, Dan Roth. 2022-10-11. Cross-Lingual Speaker Identification Using Distant Supervision. https://arxiv.org/abs/2210.05780

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