Mitigating the Linguistic Gap with Phonemic Representations for Robust Multilingual Language Understanding
Abstract: Approaches to improving multilingual language understanding often require multiple languages during the training phase, rely on complicated training techniques, and---importantly---struggle with significant performance gaps between high-resource and low-resource languages. We hypothesize that the performance gaps between languages are affected by linguistic gaps between those languages and provide a novel solution for robust multilingual language modeling by employing phonemic representations (specifically, using phonemes as input tokens to LMs rather than subwords). We present quantitative evidence from three cross-lingual tasks that demonstrate the effectiveness of phonemic representation, which is further justified by a theoretical analysis of the cross-lingual performance gap.
Paper Type: short
Research Area: Multilinguality and Language Diversity
Contribution Types: NLP engineering experiment, Approaches to low-resource settings
Languages Studied: English,Swahili,Urdu,French,German,Korean,Ukrainian,Hindi
Preprint Status: We are considering releasing a non-anonymous preprint in the next two months (i.e., during the reviewing process).
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A1 Elaboration For Yes Or No: Section 6
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A2 Elaboration For Yes Or No: We have no potential risks are expected.
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A3 Elaboration For Yes Or No: Abstract, Section 1
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B1 Elaboration For Yes Or No: Section 3
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B2 Elaboration For Yes Or No: Appendix A
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B3 Elaboration For Yes Or No: Section 3.2 Aside from providing license information and its example usage, there was no intended use specified. We utilized the datasets as they were intended to be, for non-commercial research purpose, whether or not it was specified.
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B5 Elaboration For Yes Or No: Appendix A.
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B6 Elaboration For Yes Or No: Appendix A.
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C1 Elaboration For Yes Or No: Appendix A.
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C2 Elaboration For Yes Or No: Appendix A.
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C3 Elaboration For Yes Or No: Appendix A
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C4 Elaboration For Yes Or No: Section A.3
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