Knowledge Commons of Institute of Automation,CAS
A Large-Scale Chinese Multimodal NER Dataset with Speech Clues | |
Sui DB(隋典伯)1,2![]() ![]() ![]() ![]() ![]() | |
2021-08 | |
会议名称 | Proceedings 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) |
会议日期 | 2021-8 |
会议地点 | Online |
摘要 | 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 http://github.com/DianboWork/CNERTA. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48931 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, CAS 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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2021.acl-long.218.pd(749KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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