“语音命名实体识别(Speech Named Entity Recognition, SNER)旨在从音频中识别出语音中命名实体的边界、种类和内容,是口语理解中的重要任务之一。直接从语音中识别出命名实体,即端到端方法是SNER目前的主流方法。但是语音命名实体识别的训练语料较少,端到端模型存在以下问题:(1)在跨领域识别的情况下模型的识别效果会有大幅度的下降。(2)模型在识别过程中会因同音词等现象对命名实体漏标、错标,进一步影响命名实体识别的准确性。针对问题(1),本文提出使用预训练实体识别模型构建语音实体识别的训练语料。针对问题(2),本文提出采用预训练语言模型对语音命名实体识别的N-BEST列表重打分,利用预训练模型中的外部知识帮助端到端模型挑选出最好的结果。为了验证模型的领域迁移能力,本文标注了少样本口语型数据集MAGICDATA-NER,在此数据上的实验表明,本文提出的方法相对于传统方法在F1值上有43.29%的提高。”

This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge. We participated in three tracks: the Zh-En text-to-text track, the Zh-En audio-to-text track and the En-Es test-to-text track. In our system, wait-k is employed to train prefix-to-prefix translation models. We integrate streaming chunking to detect boundaries as the source streaming read in. We further improve our system with data selection, data-augmentation and R-drop training methods. Results show that our wait-k implementation outperforms organizer’s baseline by 8 BLEU score at most, and our proposed streaming chunking method further improves about 2 BLEU in low latency regime.