Papers by En-Shiun Lee
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation (2024.findings-naacl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) methods are important in low-resource language (LRL) Neural Machine Translation (NMT) but their practical effectiveness varies significantly across different languages. |
| Approach: | They evaluated the performance of 8 parameters-efficient fine-tuning methods with 15 architectures using the SacreBLEU score. |
| Outcome: | The Houlsby+Inversion adapter outperforms the baseline architectures in both in-domain and out-domain tests and the Houlson+Inverter achieves the best performance overall. |
AfriInstruct: Instruction Tuning of African Languages for Diverse Tasks (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) for African languages perform worse compared to high-resource languages. |
| Approach: | They propose a model that specializes in instruction-tuning of multiple African languages covering various tasks. |
| Outcome: | The proposed model outperforms GPT-3.5-Turbo and other models of similar size in multiple tasks. |
Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation? (2022.findings-acl)
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En-Shiun Lee, Sarubi Thillainathan, Shravan Nayak, Surangika Ranathunga, David Adelani, Ruisi Su, Arya McCarthy
| Challenge: | Pre-trained multilingual sequence-to-sequence models like mBART and mT5 can be used to translate low-resource languages, but their practical application is unclear. |
| Approach: | They conduct an empirical experiment in 10 languages to determine what can pre-trained multilingual sequence-to-sequence models like mBART do to translate low-resource languages? |
| Outcome: | The proposed models are robust to domain differences, but translations for unseen and typologically distant languages remain below 3.0 BLEU. |
Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity (2024.findings-eacl)
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Eric Khiu, Hasti Toossi, Jinyu Liu, Jiaxu Li, David Anugraha, Juan Flores, Leandro Roman, A. Seza Doğruöz, En-Shiun Lee
| Challenge: | Existing approaches for predicting the performance of NLP models for low-resource languages (LRLs) focus on high-resourced languages, overlooking LRLs and domain shifts. |
| Approach: | They investigate the impact of domain similarity on predicting performance of machine translation models in low-resource languages. |
| Outcome: | The results show that domain similarity has the most important impact on predicting the performance of Machine Translation models. |
A Reproducibility Study on Quantifying Language Similarity: The Impact of Missing Values in the URIEL Knowledge Base (2024.naacl-srw)
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| Challenge: | URIEL aggregates linguistic information for 4,005 languages and computes distances based on this information. |
| Approach: | They propose to use a typological knowledge base to quantify language similarity to investigate URIEL's ambiguity in calculating language distances and handling missing values. |
| Outcome: | The URIEL knowledge base does not provide information about typological features for 31% of the languages it represents, undermining the reliability of the database, especially on low-resource languages. |
SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects (2024.eacl-long)
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David Adelani, Hannah Liu, Xiaoyu Shen, Nikita Vassilyev, Jesujoba Alabi, Yanke Mao, Haonan Gao, En-Shiun Lee
| Challenge: | despite progress in building multilingual language models evaluation is limited to a few languages with available datasets . despite this, we create a large-scale open-sourced benchmark dataset for topic classification in 205 languages and dialects to address the lack of evaluation dataset for Natural Language Understanding (NLU). |
| Approach: | They create a large-scale open-sourced benchmark dataset for topic classification in 205 languages and dialects to address the lack of evaluation dataset for Natural Language Understanding (NLU). |
| Outcome: | The proposed dataset addresses the lack of evaluation dataset for Natural Language Understanding (NLU) for many languages, it is the first publicly available evaluation dataset. |