In this paper, we address the data scarcity problem in automatic data-driven
glossing for low-resource languages by coordinating multiple sources of
linguistic expertise. We supplement models with translations at both the token
and sentence level as well as leverage the extensive linguistic capability of
modern LLMs. Our enhancements lead to an average absolute improvement of
5%-points in word-level accuracy over the previous state of the art on a
typologically diverse dataset spanning six low-resource languages. The
improvements are particularly noticeable for the lowest-resourced language
Gitksan, where we achieve a 10%-point improvement. Furthermore, in a simulated
ultra-low resource setting for the same six languages, training on fewer than
100 glossed sentences, we establish an average 10%-point improvement in
word-level accuracy over the previous state-of-the-art system.

通过协调多个语言专业知识源，我们解决了低资源语言自动数据驱动预览中的数据稀缺问题，并在词级准确性方面获得了 5 个百分点的平均绝对改进，对横跨六种低资源语言的多样化数据集而言，这些增强在最低资源的 Gitksan 语言中表现尤为明显，我们实现了 10 个百分点的改进。此外，在相同六种语言的模拟超低资源环境中，仅在少于 100 个含义标注句子上训练下，我们在词级准确性方面获得了 10 个百分点的平均改进，超越了以往最先进系统的水平。

多个来源胜过一个：在低资源词汇标注中整合外部知识

Multiple Sources are Better Than One: Incorporating External Knowledge  in Low-Resource Glossing

Hate detection has long been a challenging task for the NLP community. The
task becomes complex in a code-mixed environment because the models must
understand the context and the hate expressed through language alteration.
Compared to the monolingual setup, we see very less work on code-mixed hate as
large-scale annotated hate corpora are unavailable to make the study. To
overcome this bottleneck, we propose using native language hate samples. We
hypothesise that in the era of multilingual language models (MLMs), hate in
code-mixed settings can be detected by majorly relying on the native language
samples. Even though the NLP literature reports the effectiveness of MLMs on
hate detection in many cross-lingual settings, their extensive evaluation in a
code-mixed scenario is yet to be done. This paper attempts to fill this gap
through rigorous empirical experiments. We considered the Hindi-English
code-mixed setup as a case study as we have the linguistic expertise for the
same. Some of the interesting observations we got are: (i) adding native hate
samples in the code-mixed training set, even in small quantity, improved the
performance of MLMs for code-mixed hate detection, (ii) MLMs trained with
native samples alone observed to be detecting code-mixed hate to a large
extent, (iii) The visualisation of attention scores revealed that, when native
samples were included in training, MLMs could better focus on the hate emitting
words in the code-mixed context, and (iv) finally, when hate is subjective or
sarcastic, naively mixing native samples doesn't help much to detect code-mixed
hate. We will release the data and code repository to reproduce the reported
results.

在这篇论文中，我们通过严格的实证实验填补了多语言语言模型在混合代码环境中对恶意检测的评估不足，并观察到加入少量本地恶意样本以及仅使用本地样本对于提高混合代码恶意检测的效果以及可视化注意力对于定位表达恶意意图的词语在混合代码语境中是有帮助的。

通过本地样本混合改善混合语言仇恨检测：印地语 - 英语混合场景的案例研究

Improving code-mixed hate detection by native sample mixing: A case  study for Hindi-English code-mixed scenario

Annotating a multilingual code-switched corpus is a painstaking process
requiring specialist linguistic expertise. This is partly due to the large
number of language combinations that may appear within and across utterances,
which might require several annotators with different linguistic expertise to
consider an utterance sequentially. This is time-consuming and costly. It would
be useful if the spoken languages in an utterance and the boundaries thereof
were known before annotation commences, to allow segments to be assigned to the
relevant language experts in parallel. To address this, we investigate the
development of a continuous multilingual language diarizer using fine-tuned
speech representations extracted from a large pre-trained self-supervised
architecture (WavLM). We experiment with a code-switched corpus consisting of
five South African languages (isiZulu, isiXhosa, Setswana, Sesotho and English)
and show substantial diarization error rate improvements for language families,
language groups, and individual languages over baseline systems.

探讨利用大型自我监督预训练架构（WavLM）从已经训练好的语音表示中提取信息，以发展连续多语言语言标示系统，用于多语言转换的标注过程，在南非五种语言（isiZulu、isiXhosa、Setswana、Sesotho 和英语）的语料库中展示出语言家族、语言群和个体语言的大幅改进。