在口语命名实体识别系统中利用跨语言迁移学习
Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition Systems
AI总结：
针对口语NER研究不足及低资源场景问题，采用流水线与E2E方法开展多语言迁移学习，验证跨语言迁移有效性及E2E模型优势。
摘要：
近期命名实体识别（NER）的进展显著提升了文本分类能力。本文聚焦口语NER，明确面向口语文档检索这一因缺乏口语场景综合数据集而未被广泛研究的领域。此外，低资源场景下跨语言迁移学习的潜力有待进一步探究。在本研究中，我们采用流水线（pipeline）和端到端（End-to-End, E2E）两种方法，在荷兰语、英语和德语之间应用迁移学习技术。我们在自定义伪标注数据集上使用Wav2Vec2 XLS-R模型，评估跨语言系统的适配性。我们通过探索不同架构配置，评估了这些系统在口语NER中的鲁棒性。结果表明，E2E模型优于流水线模型，在标注资源有限时尤为明显。此外，从德语到荷兰语的迁移学习相比独立荷兰语E2E系统性能提升7%，相比荷兰语流水线模型提升4%。我们的研究结果凸显了跨语言迁移在口语NER中的有效性，并强调需要收集更多数据以改进这类系统。
英文摘要：
Recent Named Entity Recognition (NER) advancements have significantly enhanced text classification capabilities. This paper focuses on spoken NER, aimed explicitly at spoken document retrieval, an area not widely studied due to the lack of comprehensive datasets for spoken contexts. Additionally, the potential for cross-lingual transfer learning in low-resource situations deserves further investigation. In our study, we applied transfer learning techniques across Dutch, English, and German using both pipeline and End-to-End (E2E) approaches. We employed Wav2Vec2 XLS-R models on custom pseudo-annotated datasets to evaluate the adaptability of cross-lingual systems. Our exploration of different architectural configurations assessed the robustness of these systems in spoken NER. Results showed that the E2E model was superior to the pipeline model, particularly with limited annotation resources. Furthermore, transfer learning from German to Dutch improved performance by 7% over the standalone Dutch E2E system and 4% over the Dutch pipeline model. Our findings highlight the effectiveness of cross-lingual transfer in spoken NER and emphasize the need for additional data collection to improve these systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
