Exploring Spoken Named Entity Recognition: A Cross-Lingual Perspective
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Recent advancements in Named Entity Recognition (NER) have significantly improved the identification of entities in textual data. However, spoken NER, a specialized field of spoken document retrieval, lags behind due to its limited research and scarce datasets. Moreover, cross-lingual transfer learning in spoken NER has remained unexplored. This paper utilizes transfer learning across Dutch, English, and German using pipeline and End-to-End (E2E) schemes. We employ Wav2Vec2-XLS-R models on custom pseudo-annotated datasets and investigate several architectures for the adaptability of cross-lingual systems. Our results demonstrate that End-to-End spoken NER outperforms pipeline-based alternatives over our limited annotations. Notably, transfer learning from German to Dutch surpasses the Dutch E2E system by 7 Dutch pipeline system by 4 transfer learning in spoken NER but also sets promising outcomes for future evaluations, hinting at the need for comprehensive data collection to augment the results.
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