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A Latent Morphology Model for Open-Vocabulary Neural Machine Translation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LMM instead of the strongest baseline, Hierarch. | English-to-Arabic NMT · English-Arabic TED (in-domain) | BLEU: +0.51 BLEU points | Helped |
| Use LMM instead of the strongest baseline, Char.s | English-to-Czech NMT · English-Czech TED (in-domain) | BLEU: 16.94 -> 16.97 | Helped |
| Use LMM instead of the strongest baseline, Subwords | English-Czech NMT · English-Czech TED (in-domain) | t-BLEU: 54.22 -> 50.35 | Hurt |
| Use LMM instead of the strongest baseline, Subwords | English-Czech NMT · English-Czech TED (in-domain) | chrF3: 0.4123 -> 0.4095 | Hurt |
| Use LMM instead of the strongest baseline, Char.s | English-to-Turkish NMT · English-Turkish TED (in-domain) | BLEU: +0.30 BLEU points | Helped |
| Use LMM instead of the strongest baseline, Char.s | English-to-Turkish NMT · English-Turkish multi-domain | BLEU: 10.42 -> 11.48 | Helped |
| Set all inflectional features f to 0 (ablation) | LMM · Turkish inflection generation in context | correct inflection generation | Hurt |
| Vary the inflectional feature values while keeping the lemma fixed (ablation) | LMM · Turkish word-form generation | surface-form variation | Helped |
Closest papers by meaning
- Improved English to Russian Translation by Neural Suffix Prediction (AAAI 2018 · similarity 0.88)
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- From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables (AAAI 2022 · similarity 0.87)
- Morphological Segmentation with Window LSTM Neural Networks (AAAI 2016 · similarity 0.87)
- A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models (ICLR 2024 · similarity 0.86)
- Exploring the Translation Mechanism of Large Language Models (NeurIPS 2025 · similarity 0.85)
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