CASIA OpenIR  > 多模态人工智能系统全国重点实验室  > 自然语言处理
A Large-Scale Chinese Multimodal NER Dataset with Speech Clues
Sui DB(隋典伯)1,2; Zhengkun Tian1,2; Yubo Chen1,2; Kang Liu1,2; Jun Zhao1,2
Conference NameProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Conference Date2021-8
Conference PlaceOnline

In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents. To achieve this, we construct a large-scale human-annotated Chinese multimodal NER dataset, named CNERTA. Our corpus totally contains 42,987 annotated sentences accompanying by 71 hours of speech data. Based on this dataset, we propose a family of strong and representative baseline models, which can leverage textual features or multimodal features. Upon these baselines, to capture the natural monotonic alignment between the textual modality and the acoustic modality, we further propose a simple multimodal multitask model by introducing a speech-to-text alignment auxiliary task. Through extensive experiments, we observe that: (1) Progressive performance boosts as we move from unimodal to multimodal, verifying the necessity of integrating speech clues into Chinese NER. (2) Our proposed model yields state-of-the-art (SoTA) results on CNERTA, demonstrating its effectiveness. For further research, the annotated dataset is publicly available at

Document Type会议论文
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, CAS
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Sui DB,Zhengkun Tian,Yubo Chen,et al. A Large-Scale Chinese Multimodal NER Dataset with Speech Clues[C],2021.
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