Scaling up the End-to-End (E2E) approach for spoken Named Entity Recognition (NER) presents several challenges and limitations, particularly concerning data requirements and computational resources. One of the primary challenges is the need for large, high-quality annotated datasets. E2E models require extensive training data to learn the complex relationships between speech signals and their corresponding transcriptions with entity markers. In many cases, especially for low-resource languages, such datasets are scarce or non-existent, making it difficult to train effective models.
Moreover, the process of creating annotated datasets is often labor-intensive and costly, as it involves not only transcribing spoken content but also accurately tagging entities. This complexity can hinder the scalability of E2E systems, as the time and resources required for data collection and annotation may not be feasible for all languages or domains.
In terms of computational resources, E2E models typically demand significant processing power and memory, especially when utilizing deep learning architectures like Transformers. Training these models on large datasets can lead to high computational costs, which may be prohibitive for smaller research teams or organizations. Additionally, the need for specialized hardware, such as GPUs or TPUs, can further limit accessibility.
Lastly, the integration of E2E systems into existing workflows may pose challenges, particularly in environments where traditional pipeline approaches are already established. Transitioning to an E2E framework requires not only technical adjustments but also a shift in mindset regarding how spoken language processing is approached.