Papers by Yoonjung Choi
Modularized Multilingual NMT with Fine-grained Interlingua (2024.naacl-long)
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| Challenge: | Neural Machine Translation (MNMT) systems lack layer-sharing to generate interlingua features . however, layer-share structure does not guarantee explicit propagation of language-specific features to respective decoders. |
| Approach: | They propose to share top of language-specific encoder layers to enable interlingua features . their method demonstrates an improved average BLEU score by "+2.90" in En-to-Any directions . |
| Outcome: | The proposed approach improves the BLEU score by "+2.90" in En-to-Any directions and by "+1.06" in zero-shot translation. |
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation (2020.findings-emnlp)
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Insoo Chung, Byeongwook Kim, Yoonjung Choi, Se Jung Kwon, Yongkweon Jeon, Baeseong Park, Sangha Kim, Dongsoo Lee
| Challenge: | Quantization is an effective technique to address heavy computation load and memory overhead during inference. |
| Approach: | They propose a low-bit quantization strategy to represent Transformer weights by an extremely low number of bits. |
| Outcome: | The proposed model achieves 11.8 smaller model size than baseline model, with less than -0.5 BLEU. |