One-Shot Neural Cross-Lingual Transfer for Paradigm Completion
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Abstract (arXiv)
We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task. We use labeled data from a high-resource language to increase performance on a low-resource language. In experiments on 21 language pairs from four different language families, we obtain up to 58% higher accuracy than without transfer and show that even zero-shot and one-shot learning are possible. We further find that the degree of language relatedness strongly influences the ability to transfer morphological knowledge.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Mar 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2022Morphological Processing of Low-Resource Languages: Where We Are and What's Nextcited by 4
- 2021Computational Morphology with Neural Network Approachescited by 2
- 2021Minimal Supervision for Morphological Inflectioncited by 1
- 2026Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corporacited by 1
- 2026Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora21 May 2026
- 2022Morphological Processing of Low-Resource Languages: Where We Are and What's Next16 Mar 2022
- 2021Computational Morphology with Neural Network Approaches19 May 2021
- 2021Minimal Supervision for Morphological Inflection17 Apr 2021