Knowledge Commons of Institute of Automation,CAS
Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding | |
Shuxian, Zou1,2![]() ![]() ![]() ![]() | |
2022-05 | |
会议名称 | Findings of the Association for Computational Linguistics: ACL 2022 |
会议日期 | 2022-5 |
会议地点 | Dublin, Ireland |
摘要 | Decoding language from non-invasive brain activity has attracted increasing attention from both researchers in neuroscience and natural language processing. Due to the noisy nature of brain recordings, existing work has simplified brain-to-word decoding as a binary classification task which is to discriminate a brain signal between its corresponding word and a wrong one. This pairwise classification task, however, cannot promote the development of practical neural decoders for two reasons. First, it has to enumerate all pairwise combinations in the test set, so it is inefficient to predict a word in a large vocabulary. Second, a perfect pairwise decoder cannot guarantee the performance on direct classification. To overcome these and go a step further to a realistic neural decoder, we propose a novel Cross-Modal Cloze (CMC) task which is to predict the target word encoded in the neural image with a context as prompt. Furthermore, to address this task, we propose a general approach that leverages the pre-trained language model to predict the target word. To validate our method, we perform experiments on more than 20 participants from two brain imaging datasets. Our method achieves 28.91% top-1 accuracy and 54.19% top-5 accuracy on average across all participants, significantly outperforming several baselines. This result indicates that our model can serve as a state-of-the-art baseline for the CMC task. More importantly, it demonstrates that it is feasible to decode a certain word within a large vocabulary from its neural brain activity. |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48646 |
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.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.National Laboratory of Pattern Recognition, Institute of Automation, CAS 3.CAS Center for Excellence in Brain Science and Intelligence Technology |
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 | Shuxian, Zou,Shaonan, Wang,Jiajun, Zhang,et al. Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding[C],2022. |
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2022 - Cross-Modal C(375KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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