Prior work on multilingual sentence embedding has demonstrated that the
efficient use of natural language inference (NLI) data to build
high-performance models can outperform conventional methods. However, the
potential benefits from the recent ``exponential'' growth of language models
with billions of parameters have not yet been fully explored. In this paper, we
introduce Multilingual Sentence T5 (m-ST5), as a larger model of NLI-based
multilingual sentence embedding, by extending Sentence T5, an existing
monolingual model. By employing the low-rank adaptation (LoRA) technique, we
have achieved a successful scaling of the model's size to 5.7 billion
parameters. We conducted experiments to evaluate the performance of sentence
embedding and verified that the method outperforms the NLI-based prior
approach. Furthermore, we also have confirmed a positive correlation between
the size of the model and its performance. It was particularly noteworthy that
languages with fewer resources or those with less linguistic similarity to
English benefited more from the parameter increase. Our model is available at
this https URL

我们介绍了基于 NLI 的多语言句子嵌入模型 m-ST5，通过扩展现有的单语模型 Sentence T5 以低秩适应（LoRA）技术成功将模型参数规模扩展到 57 亿，并通过实验证实方法优于基于 NLI 的先前方法，尤其是对资源较少或与英语相似性较低的语言受益更多。

多语句 - T5：可扩展的多语句编码器适用于多语言应用

Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual  Applications

We learn a joint multilingual sentence embedding and use the distance between
sentences in different languages to filter noisy parallel data and to mine for
parallel data in large news collections. We are able to improve a competitive
baseline on the WMT'14 English to German task by 0.3 BLEU by filtering out 25%
of the training data. The same approach is used to mine additional bitexts for
the WMT'14 system and to obtain competitive results on the BUCC shared task to
identify parallel sentences in comparable corpora. The approach is generic, it
can be applied to many language pairs and it is independent of the architecture
of the machine translation system.

通过联合多语句嵌入学习并利用在不同语言中句子之间的距离来过滤嘈杂的平行数据和在大型新闻集合中挖掘平行数据。不同于翻译系统的体系结构，这种方法可以应用于多种语言对，并在 BUCC 共享任务中获得有竞争力的结果，用于识别可比较语料库中的平行句子。