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Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation

arXiv 2209.15164 ↗First posted 30 Sep 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

Buddhism is an influential religion with a long-standing history and profound philosophy. Nowadays, more and more people worldwide aspire to learn the essence of Buddhism, attaching importance to Buddhism dissemination. However, Buddhist scriptures written in classical Chinese are obscure to most people and machine translation applications. For instance, general Chinese-English neural machine translation (NMT) fails in this domain. In this paper, we proposed a novel approach to building a practical NMT model for Buddhist scriptures. The performance of our translation pipeline acquired highly promising results in ablation experiments under three criteria.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 30 Sep 2022.

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References · 13

  1. 2020Language-agnostic BERT Sentence Embeddingcited by 621
  2. 2022LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learningcited by 228
  3. 2020Nearest Neighbor Machine Translationcited by 209
  4. 2019Facebook FAIR's WMT19 News Translation Task Submissioncited by 179
  5. 2021Improving Sign Language Translation with Monolingual Data by Sign Back-Translationcited by 177
  6. 2020Translation Artifacts in Cross-lingual Transfer Learningcited by 95
  7. 2021Adaptive Nearest Neighbor Machine Translationcited by 58
  8. 2022Efficient Cluster-Based k-Nearest-Neighbor Machine Translationcited by 24
  9. 2019Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translationcited by 23
  10. 2020Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentencescited by 19
  11. 2021A Survey on Low-Resource Neural Machine Translationcited by 16
  12. 2022nnSpeech: Speaker-Guided Conditional Variational Autoencoder for Zero-shot Multi-speaker Text-to-Speechcited by 10
1 more
  1. 2020Pronoun-Targeted Fine-tuning for NMT with Hybrid Lossescited by 5

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