Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning
This work addresses readability issues for Spanish customer support services, but it is incremental as it applies known transfer learning methods to a specific domain.
The paper tackled the problem of poor readability in unpunctuated Spanish customer support transcripts by developing a punctuation restoration system using transfer learning, achieving improved accuracy through domain adaptation and cross-lingual strategies.
Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability. In addition, building a punctuation restoration system is challenging for low-resource languages, especially for domain-specific applications. In this paper, we propose a Spanish punctuation restoration system designed for a real-time customer support transcription service. To address the data sparsity of Spanish transcripts in the customer support domain, we introduce two transfer-learning-based strategies: 1) domain adaptation using out-of-domain Spanish text data; 2) cross-lingual transfer learning leveraging in-domain English transcript data. Our experiment results show that these strategies improve the accuracy of the Spanish punctuation restoration system.