This research addresses the challenge of developing speech applications for
zero-resource languages that lack labelled data. It specifically uses acoustic
word embedding (AWE) -- fixed-dimensional representations of variable-duration
speech segments -- employing multilingual transfer, where labelled data from
several well-resourced languages are used for pertaining. The study introduces
a new neural network that outperforms existing AWE models on zero-resource
languages. It explores the impact of the choice of well-resourced languages.
AWEs are applied to a keyword-spotting system for hate speech detection in
Swahili radio broadcasts, demonstrating robustness in real-world scenarios.
Additionally, novel semantic AWE models improve semantic query-by-example
search.

该研究针对缺乏已标注数据的零资源语言开发语音应用的挑战，特别使用声学词嵌入（AWE），通过多语言转移利用来自几种资源丰富的语言的已标注数据。该研究介绍了一种新的神经网络，优于现有的 AWE 模型在零资源语言上的表现，研究还探讨了资源丰富语言的选择对结果的影响。AWE 应用于斯瓦希里语广播中的仇恨言论检测的关键词识别系统，展示了在实际场景中的稳健性。此外，新颖的语义 AWE 模型提高了语义查询示例搜索的性能。

零资源语言的多语种音频词向量

Multilingual acoustic word embeddings for zero-resource languages

For named entity recognition (NER) in zero-resource languages, utilizing
knowledge distillation methods to transfer language-independent knowledge from
the rich-resource source languages to zero-resource languages is an effective
means. Typically, these approaches adopt a teacher-student architecture, where
the teacher network is trained in the source language, and the student network
seeks to learn knowledge from the teacher network and is expected to perform
well in the target language. Despite the impressive performance achieved by
these methods, we argue that they have two limitations. Firstly, the teacher
network fails to effectively learn language-independent knowledge shared across
languages due to the differences in the feature distribution between the source
and target languages. Secondly, the student network acquires all of its
knowledge from the teacher network and ignores the learning of target
language-specific knowledge. Undesirably, these limitations would hinder the
model's performance in the target language. This paper proposes an unsupervised
prototype knowledge distillation network (ProKD) to address these issues.
Specifically, ProKD presents a contrastive learning-based prototype alignment
method to achieve class feature alignment by adjusting the distance among
prototypes in the source and target languages, boosting the teacher network's
capacity to acquire language-independent knowledge. In addition, ProKD
introduces a prototypical self-training method to learn the intrinsic structure
of the language by retraining the student network on the target data using
samples' distance information from prototypes, thereby enhancing the student
network's ability to acquire language-specific knowledge. Extensive experiments
on three benchmark cross-lingual NER datasets demonstrate the effectiveness of
our approach.

该研究提出了一种无监督的原型知识蒸馏网络（ProKD）来提高模型在跨语言命名实体识别问题上的表现。该方法通过原型对齐和基于原型的自训练两种方法，让源语言和目标语言的类别特征得到对齐，同时提高模型获取跨语言通用知识和目标语言特定知识的能力。在三个基准数据集上的实验表明该方法的有效性。

ProKD: 一种无监督的原型知识蒸馏网络，用于零资源跨语言命名实体识别

ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition

Neural machine translation~(NMT) is ineffective for zero-resource languages.
Recent works exploring the possibility of unsupervised neural machine
translation (UNMT) with only monolingual data can achieve promising results.
However, there are still big gaps between UNMT and NMT with parallel
supervision. In this work, we introduce a multilingual unsupervised NMT
(\method) framework to leverage weakly supervised signals from high-resource
language pairs to zero-resource translation directions. More specifically, for
unsupervised language pairs \texttt{En-De}, we can make full use of the
information from parallel dataset \texttt{En-Fr} to jointly train the
unsupervised translation directions all in one model. \method is based on
multilingual models which require no changes to the standard unsupervised NMT.
Empirical results demonstrate that \method significantly improves the
translation quality by more than 3 BLEU score on six benchmark unsupervised
translation directions.

本研究提出了一种跨语言无监督神经机器翻译框架，利用来自高资源语言对的弱监督信号，以提高零资源翻译质量。该框架基于多语言模型，不需要对标准无监督神经机器翻译进行改动，实验结果表明使用该框架可以在六个基准无监督翻译方向上将翻译质量提高超过 3 个 BLEU 分数。