面向危机相关社交媒体文本的语义增强跨语言句子嵌入
Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts
AI总结：
针对危机社交媒体文本多语言嵌入的语义与跨语言对齐问题，提出CT-XLMR-SE和CT-mBERT-SE多语言句子编码器，支持超50种语言并实现同向量空间内的语义邻近，为多语言危机文本嵌入提供稳健基线。
摘要：
对危机相关社交媒体文本进行语义搜索和聚类等任务有助于我们理解危机话语，从而辅助决策与针对性干预。预训练语言模型提升了危机信息学领域的性能，但其上下文嵌入缺乏语义有效性。尽管CrisisTransformers家族包含一个用于解决语义性问题的句子编码器，它仍然是单语的，仅处理英文文本。此外，为不同语言使用独立模型会导致嵌入处于不同的向量空间中，在比较多语言文本间的语义相似性时引入挑战。因此，我们提出多语言句子编码器（CT-XLMR-SE和CT-mBERT-SE），能够对超过50种语言的危机相关社交媒体文本进行嵌入，使得具有相似含义的文本在同一向量空间中紧密相邻，而不受语言多样性的影响。在句子编码和句子匹配任务中的结果令人鼓舞，表明这些模型可作为嵌入多语言危机相关社交媒体文本的稳健基线。模型已在以下地址公开：https://huggingface.co/crisistransformers。
英文摘要：
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: https://huggingface.co/crisistransformers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
