State-of-the-art multilingual models depend on vocabularies that cover all of
the languages the model will expect to see at inference time, but the standard
methods for generating those vocabularies are not ideal for massively
multilingual applications. In this work, we introduce a novel procedure for
multilingual vocabulary generation that combines the separately trained
vocabularies of several automatically derived language clusters, thus balancing
the trade-off between cross-lingual subword sharing and language-specific
vocabularies. Our experiments show improvements across languages on key
multilingual benchmark tasks TyDi QA (+2.9 F1), XNLI (+2.1\%), and WikiAnn NER
(+2.8 F1) and factor of 8 reduction in out-of-vocabulary rate, all without
increasing the size of the model or data.

本文比较现有多语言模型词汇表生成方法，提出了一种新的多语言词汇表生成方法。实验证明，该方法能够提高多个语言上的推理性能，同时在不增加模型大小或数据的情况下，将语言识别率降低了 8 倍。

利用语言聚类词汇改进多语言模型

Improving Multilingual Models with Language-Clustered Vocabularies

We study training a single acoustic model for multiple languages with the aim
of improving automatic speech recognition (ASR) performance on low-resource
languages, and over-all simplifying deployment of ASR systems that support
diverse languages. We perform an extensive benchmark on 51 languages, with
varying amount of training data by language(from 100 hours to 1100 hours). We
compare three variants of multilingual training from a single joint model
without knowing the input language, to using this information, to multiple
heads (one per language cluster). We show that multilingual training of ASR
models on several languages can improve recognition performance, in particular,
on low resource languages. We see 20.9%, 23% and 28.8% average WER relative
reduction compared to monolingual baselines on joint model, joint model with
language input and multi head model respectively. To our knowledge, this is the
first work studying multilingual ASR at massive scale, with more than 50
languages and more than 16,000 hours of audio across them.

本文探讨了利用单一声学模型进行多种语言训练，以提高低资源语言的自动语音识别性能，并简化支持多种语言的 ASR 系统的部署。作者在 51 种语言上进行广泛的基准测试和比较，表明与单语言训练相比，多语言训练的 ASR 模型可以提高识别性能，特别是对于低资源语言。与单语言基线相比，联合模型、具有语言输入的联合模型和多头模型的平均 WER 相对减少 20.9％、23％和 28.8％。据我们所知，这是第一次研究超过 50 种语言和超过 16,000 小时声音跨其的多语言 ASR 的大规模研究。