通过音素转写集成增强跨语言迁移
Enhancing Cross-lingual Transfer via Phonemic Transcription Integration
AI总结：
本文提出PhoneXL框架，通过引入音素转写作为额外模态并设计无监督对齐目标，增强不同书写系统语言间的跨语言迁移，并在CJKV语言的词元级任务上取得一致提升。
摘要：
以往的跨语言迁移方法仅限于通过文字脚本进行正字法表示学习。这一局限阻碍了跨语言迁移，并偏向于共享相似知名文字系统的语言。为缩小来自不同书写系统语言之间的差距，我们提出PhoneXL，一个将音素转写作为传统正字法转写之外的额外语言模态纳入跨语言迁移的框架。具体而言，我们提出无监督对齐目标，以捕获（1）两种不同模态之间的局部一对一对齐，（2）通过多模态上下文进行对齐，以利用来自额外模态的信息，以及（3）通过多语言上下文进行对齐，其中纳入了额外的双语词典。我们还发布了首个音素-正字法对齐数据集，涵盖研究不足但相互关联的中-日-韩-越（CJKV）语言中的两个词元级任务（命名实体识别和词性标注）。我们的初步研究表明，音素转写提供了正字法之外的关键信息，以增强跨语言迁移并弥合CJKV语言之间的差距，从而在基于正字法的多语言预训练语言模型上实现了跨语言词元级任务的一致改进。
英文摘要：
Previous cross-lingual transfer methods are restricted to orthographic representation learning via textual scripts. This limitation hampers cross-lingual transfer and is biased towards languages sharing similar well-known scripts. To alleviate the gap between languages from different writing scripts, we propose PhoneXL, a framework incorporating phonemic transcriptions as an additional linguistic modality beyond the traditional orthographic transcriptions for cross-lingual transfer. Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated. We also release the first phonemic-orthographic alignment dataset on two token-level tasks (Named Entity Recognition and Part-of-Speech Tagging) among the understudied but interconnected Chinese-Japanese-Korean-Vietnamese (CJKV) languages. Our pilot study reveals phonemic transcription provides essential information beyond the orthography to enhance cross-lingual transfer and bridge the gap among CJKV languages, leading to consistent improvements on cross-lingual token-level tasks over orthographic-based multilingual PLMs.
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。
