Papers with Khmer
A Simple and Fast Strategy for Handling Rare Words in Neural Machine Translation (2022.aacl-srw)
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| Challenge: | Neural Machine Translation (NMT) has been gaining popularity due to its ability to bias in highfrequency words, low-frequency words have little chance of being considered in the inference process. |
| Approach: | They propose a strategy for integrating constraints during the training and decoding process to improve the translation of rare words. |
| Outcome: | The proposed approach improves translation of rare words in high and low-resource translation tasks, showing improvements of up to +1.8 BLEU scores over baseline systems. |
Robust Neural Machine Translation for Abugidas by Glyph Perturbation (2024.eacl-short)
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| Challenge: | Neural machine translation systems are vulnerable when trained on limited data. |
| Approach: | They propose to add noise to the training phase to increase robustness of NMT systems trained on limited data. |
| Outcome: | The proposed training strategy overcomes noise and improves robustness for low-resource tasks for abugida glyphs. |
SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)
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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing
| Challenge: | Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages. |
| Approach: | They propose a series of language models that specifically focuses on Southeast Asian languages. |
| Outcome: | SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations . |
Simplified Abugidas (P18-2)
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| Challenge: | Abugidas are writing systems where consonant letters represent syllables with a default vowel and other vowels are denoted by diacritics. |
| Approach: | They investigated the feasibility of recovering the original text written in an abugida after omitting subordinate diacritics and merging consonant letters with similar phonetic values. |
| Outcome: | The proposed method recovers the original text written in an abugida with 94% - 97% accuracy at the top-1 level and 98% - 99% at the bottom-4 level even after omitting most diacritics and merging the remaining 30 - 50 characters into 21 graphemes. |
PrahokBART: A Pre-trained Sequence-to-Sequence Model for Khmer Natural Language Generation (2025.coling-main)
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| Challenge: | Pre-trained sequence-to-sequence models are typically pretrained on extensive raw text corpora and fine-tuned on task-specific data. |
| Approach: | They introduce a pre-trained sequence-to-sequence model trained from scratch for Khmer using carefully curated Khmer and English corpora. |
| Outcome: | The proposed model outperforms existing models on three generative tasks and is data-efficient and effective in enhancing performance across various natural language generation tasks. |
Linking the TUFS Basic Vocabulary to the Open Multilingual Wordnet (2020.lrec-1)
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| Challenge: | The TUFS Basic Vocabulary Modules are hand created, using commonly occurring vocabulary. |
| Approach: | They propose to link the TUFS Basic Vocabulary Modules with the Open Multilingual Wordnet to create a multilingual lexicon. |
| Outcome: | The proposed lexicons can be used to evaluate existing wordnets, add data to wordnet synsets and create new open wordnet for Khmer, Korean, Lao, Mongolian, Russian, Tagalog, Urdua nd Vietnamese. |
KC4MT: A High-Quality Corpus for Multilingual Machine Translation (2022.lrec-1)
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Vinh Van Nguyen, Ha Nguyen, Huong Thanh Le, Thai Phuong Nguyen, Tan Van Bui, Luan Nghia Pham, Anh Tuan Phan, Cong Hoang-Minh Nguyen, Viet Hong Tran, Anh Huu Tran
| Challenge: | In machine translation, Vietnamese is a low-resource language, and the quality of the training corpus is very low. |
| Approach: | They propose a method for building high-quality multilingual parallel corpus in news domain . they also publicize a corpus that includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
| Outcome: | The proposed method improves the quality of multilingual machine translation in Vietnamese, Laos, and Khmer . the public version includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |