Tasks such as semantic search and clustering on crisis-related social media
texts enhance our comprehension of crisis discourse, aiding decision-making and
targeted interventions. Pre-trained language models have advanced performance
in crisis informatics, but their contextual embeddings lack semantic
meaningfulness. Although the CrisisTransformers family includes a sentence
encoder to address the semanticity issue, it remains monolingual, processing
only English texts. Furthermore, employing separate models for different
languages leads to embeddings in distinct vector spaces, introducing challenges
when comparing semantic similarities between multi-lingual texts. Therefore, we
propose multi-lingual sentence encoders (CT-XLMR-SE and CT-mBERT-SE) that embed
crisis-related social media texts for over 50 languages, such that texts with
similar meanings are in close proximity within the same vector space,
irrespective of language diversity. Results in sentence encoding and sentence
matching tasks are promising, suggesting these models could serve as robust
baselines when embedding multi-lingual crisis-related social media texts. The
models are publicly available at: this https URL

通过使用多语言句子编码器，我们提出了一种能够为 50 多种语言中的相关社交媒体文本进行嵌入的模型，以使具有相似含义的文本在相同的向量空间中靠近，无论语言多样性如何。研究结果对句子编码和匹配任务表现出良好的性能，表明这些模型可以作为嵌入多语言危机相关社交媒体文本时的强大基准模型。