Speech entity linking amis to recognize mentions from speech and link them to entities in knowledge bases. Previous work on entity linking mainly focuses on visual context and text context. In contrast, speech entity linking focuses on audio context. In this paper, we first propose the speech entity linking task. To facilitate the study of this task, we propose the first speech entity linking dataset, TED-EL. Our corpus is a high-quality, human-annotated, audio, text, and mention-entity pair parallel dataset derived from Technology, Entertainment, Design (TED) talks and includes a wide range of entity types (24 types). Based on TED-EL, we designed two types of models: ranking-based and generative speech entity linking models. We conducted experiments on the TED-EL dataset for both types of models. The results show that the ranking-based models outperform the generative models, achieving an F1 score of 60.68%.

“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.