Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning

TL;DR

This paper presents a Spanish punctuation restoration system for customer support transcripts that leverages transfer learning from both domain-specific and cross-lingual English data to improve accuracy.

Contribution

It introduces two transfer learning strategies—domain adaptation and cross-lingual transfer—for improving punctuation restoration in low-resource, domain-specific Spanish transcripts.

Findings

Transfer learning improves punctuation accuracy.

Domain adaptation enhances performance with out-of-domain data.

Cross-lingual transfer leverages English data effectively.

Abstract

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.