Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer
Approaches to improving multilingual language understanding often struggle with significant performance gaps between high-resource and low-resource languages. While there are efforts to align the languages in a single latent space to mitigate such gaps, how different input-level representations influence such gaps has not been investigated, particularly with phonemic inputs. We hypothesize that the performance gaps are affected by representation discrepancies between these languages, and revisit the use of phonemic representations as a means to mitigate these discrepancies. To demonstrate the effectiveness of phonemic representations, we present experiments on three representative cross-lingual tasks on 12 languages in total. The results show that phonemic representations exhibit higher similarities between languages compared to orthographic representations, and it consistently outperforms grapheme-based baseline model on languages that are relatively low-resourced. We present quantitative evidence from three cross-lingual tasks that demonstrate the effectiveness of phonemic representations, and it is further justified by a theoretical analysis of the cross-lingual performance gap.
Code (0)
등록된 구현이 없습니다.
Tasks
Cross-Lingual TransferLanguage ModellingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Enhancing Cross-lingual Transfer via Phonemic Transcription Integration
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-know…
Cross-Lingual Transfernamed-entity-recognitionNamed Entity RecognitionPart-Of-Speech Tagging+1Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
Existing zero-shot cross-lingual NER approaches require substantial prior knowledge of the target language, which is impractical for low-resource languages. In this paper, we propose a novel approach to NER using phonemi…
Cross-Lingual NERNERIPA-CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling
In this paper, we introduce two resources: (i) G2P+, a tool for converting orthographic datasets to a consistent phonemic representation; and (ii) IPA CHILDES, a phonemic dataset of child-centered speech across 31 langua…
Grapheme-to-Phoneme ConversionLanguage ModelingLanguage ModellingPrompting with Phonemes: Enhancing LLM Multilinguality for non-Latin Script Languages
Multilingual LLMs have achieved remarkable benchmark performance, but we find they continue to underperform on non-Latin script languages across contemporary LLM families. This discrepancy arises from the fact that LLMs …
In-Context LearningRetrievalThe taste of IPA: Towards open-vocabulary keyword spotting and forced alignment in any language
In this project, we demonstrate that phoneme-based models for speech processing can achieve strong crosslinguistic generalizability to unseen languages. We curated the IPAPACK, a massively multilingual speech corpora wit…
Keyword Spotting