论文标题
Aishell-ner：中国演讲中名为“实体识别”
AISHELL-NER: Named Entity Recognition from Chinese Speech
论文作者
论文摘要
语音中的命名实体识别（NER）是口语理解（SLU）任务之一，旨在从语音信号中提取语义信息。语音的NER通常是通过两步管道制成的，该管道包括（1）使用自动语音识别（ASR）系统处理音频，以及（2）将NER标记器应用于ASR输出。最近的作品表明，端到端（E2E）方法的能力是英语和法语演讲的NER，这本质上是实体意识到的ASR。但是，由于中文中存在的许多同音词和综合词，中国言语的NER实际上是一项更具挑战性的任务。在本文中，我们介绍了一个新的数据集Aisehll-ner，以供中国演讲中NER。进行了广泛的实验，以探索几种最新方法的性能。结果表明，可以通过将实体感知的ASR和预验证的NER标签器相结合来提高性能，该标记可以很容易地应用于现代SLU管道。该数据集可在github.com/alibaba-nlp/aishell-ner上公开获得。
Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal. NER from speech is usually made through a two-step pipeline that consists of (1) processing the audio using an Automatic Speech Recognition (ASR) system and (2) applying an NER tagger to the ASR outputs. Recent works have shown the capability of the End-to-End (E2E) approach for NER from English and French speech, which is essentially entity-aware ASR. However, due to the many homophones and polyphones that exist in Chinese, NER from Chinese speech is effectively a more challenging task. In this paper, we introduce a new dataset AISEHLL-NER for NER from Chinese speech. Extensive experiments are conducted to explore the performance of several state-of-the-art methods. The results demonstrate that the performance could be improved by combining entity-aware ASR and pretrained NER tagger, which can be easily applied to the modern SLU pipeline. The dataset is publicly available at github.com/Alibaba-NLP/AISHELL-NER.
