ੈքதͷݴޠͱσʔλྔ 7 ݴޠʢຊޠɺӳޠͳͲʣ 2191 ݴޠ 222 ݴޠ The State and Fate of Linguistic Diversity and Inclusion in the NLP World (Joshi et al., ACL 2020) Figure 1 ΑΓ
ϚϧνϦϯΨϧͳݴޠϕΫτϧͷ࡞Γํ ᶃ ର༁σʔλΛͬͯϕΫτϧΛֶश͢Δ Bilingual Word Representations with Monolingual Quality in Mind (Luong et al., LatentVar 2015) Figure 1 ΑΓ ςΩετͷपғͷ୯ޠΛ༧ଌ͢Δ͜ͱʹՃ͑ͯɺର༁ͱͳΔ୯ޠ༧ଌ͢Δɻ
ϚϧνϦϯΨϧͳ୯ޠϕΫτϧͷ֫ಘ Exploiting Similarities among Languages for Machine Translation (Mikolov et al., arXiv 2013) Word translation without parallel data (Lample et al., ICLR 2018) A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (Artetxe et al., ACL 2018)
ඞཁ͕͋Δɻ On the Limitations of Unsupervised Bilingual Dictionary Induction (Søgaard et al., ACL 2018) ͨͩ͠ɺ͍ͭͰର༁σʔλͳ͠ͰϚϧνϦϯΨϧͳ୯ޠϕΫτϧΛ ֫ಘ͢Δ͜ͱ͕͍ͭͰ͏·͘ߦ͔͘ͱ͍͏ͱͦΜͳ͜ͱͳ͘… 😢
‣ ͋Μ·Γ͍͜͠ͱߟ͑ͳͯ͘ग़དྷͯ͠·͏ɻ Q. BERT ͰϚϧνϦϯΨϧϞσϧ࡞Δ࣌Ͳ͏͢Δͷʁ BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., NAACL 2019) Figure 1 ΑΓ
ϚϧνϦϯΨϧͳ BERT Λֶश͢ΔͱͲ͏ͳΔ͔ʁ ҟͳΔݴޠ͔ΒͷτʔΫϯͷ ID ͕ॏͳΒͳ͚ΕΑ͍
‣ ยํͷݴޠͷจࣈͷϢχίʔυΛͣΒͯ͠มͳจࣈʹ͢Δ
‣ τʔΫϯͷ ID ΛͣΒ͢ ࣮ݧઃఆͷ࡞Γํ Emerging Cross-lingual Structure in Pretrained Language Models (Conneau et al., ACL 2020) Cross-Lingual Ability of Multilingual BERT: An Empirical Study (K et al., ICLR 2020)
ݴޠԣஅͷೳྗ͕֫ಘ͞ΕΔ݅ʹ͍ͭͯௐࠪ͞Ε͍ͯ Δ͕ɺ֬ఆతʹʮ͜Ε͕ॏཁʯͱ͍͏ཁૉΑ͔ͬͯ͘ ͍ͳ͍ɻ Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review (Philippy et al., ACL 2023)
-τʔΫφΠθʔγϣϯͷඇޮ͞- Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models (Ahia et al., NAACL 2023) Figure 5 ΑΓ ಉ͡༰ͷςΩετͰӳޠʹൺͯຊޠ̎ഒ΄ͲͷτʔΫϯΛফඅ͢Δɻ
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