Self-attention Based Model for Punctuation Prediction Using Word and Speech Embeddings | |
Jiangyan Yi; Jianhua Tao | |
2019 | |
会议日期 | 2019.05.12-2019.05.15 |
会议地点 | Brighton, UK |
英文摘要 | This paper proposes to use self-attention based model to predict punctuation marks for word sequences. The model is trained using word and speech embedding features which are obtained from the pre-trained Word2Vec and Speech2Vec, respectively. Thus, the model can use any kind of textual data and speech data. Experiments are conducted on English IWSLT2011 datasets. The results show that the self-attention based model trained using word and speech embedding features outperforms the previous state-of-the-art single model by up to 7.8% absolute overall F1-score. The results also show that it obtains performance improvement by up to 4.7% absolute overall F1-score against the previous best ensemble model. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40663] |
专题 | 模式识别国家重点实验室_智能交互 |
作者单位 | 1.中国科学院大学 2.中国科学院自动化研究所; |
推荐引用方式 GB/T 7714 | Jiangyan Yi,Jianhua Tao. Self-attention Based Model for Punctuation Prediction Using Word and Speech Embeddings[C]. 见:. Brighton, UK. 2019.05.12-2019.05.15. |
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