提出高效命名实体识别框架，提升推理速度同时保持高准确率。
Decoding Text Spans for Efficient and Accurate Named-Entity Recognition
- 在最后Transformer层计算跨度表示交互，减少冗余计算。
- 枚举阶段引入跨度过滤，提前剔除低概率候选。
- 相比现有方法提速显著，适合大规模部署和设备端应用。
命名实体识别（NER）是工业信息抽取流程中的关键组件，系统需在满足严格延迟与吞吐量要求的同时保证高准确率。当前先进方法多采用基于跨度的框架，通过词元编码构建跨度表示并分类候选跨度。然而，许多方法需枚举大量候选，并对每个候选使用标记增强输入，显著增加推理开销，限制了在大规模部署中的可扩展性。本文提出SpanDec，一种高效的基于跨度的NER框架，旨在解决该瓶颈。核心洞察是：跨度表示间的交互可在最终Transformer层高效计算，通过轻量解码器避免早期层的冗余计算。此外，在枚举阶段引入跨度过滤机制，提前剔除低概率候选再进行昂贵处理。在多个基准测试中，SpanDec达到与现有先进基线相当的准确率，同时提升吞吐量并降低计算成本，展现出更优的准确率-效率权衡，适用于高并发服务及设备端应用。
原文摘要 · Abstract (English)
Named Entity Recognition (NER) is a key component in industrial information extraction pipelines, where systems must satisfy strict latency and throughput constraints in addition to strong accuracy. State-of-the-art NER accuracy is often achieved by span-based frameworks, which construct span representations from token encodings and classify candidate spans. However, many span-based methods enumerate large numbers of candidates and process each candidate with marker-augmented inputs, substantially increasing inference cost and limiting scalability in large-scale deployments. In this work, we propose SpanDec, an efficient span-based NER framework that targets this bottleneck. Our main insight is that span representation interactions can be computed effectively at the final transformer stage, avoiding redundant computation in earlier layers via a lightweight decoder dedicated to span representations. We further introduce a span filtering mechanism during enumeration to prune unlikely candidates before expensive processing. Across multiple benchmarks, SpanDec matches competitive span-based baselines while improving throughput and reducing computational cost, yielding a better accuracy-efficiency trade-off suitable for high-volume serving and on-device applications.
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