# One-Shot Neural Cross-Lingual Transfer for Paradigm Completion

## TL;DR

This paper introduces a neural cross-lingual transfer method for paradigm completion, enabling high accuracy in low-resource languages through one-shot learning and demonstrating the importance of language relatedness.

## Contribution

It proposes a novel neural transfer approach for paradigm completion that works effectively with minimal data and across diverse language pairs.

## Key findings

- Up to 58% accuracy improvement with transfer
- Zero-shot and one-shot learning are feasible
- Language relatedness impacts transfer success

## Abstract

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.