Zero-resource named entity recognition (NER) severely suffers from data
scarcity in a specific domain or language. Most studies on zero-resource NER
transfer knowledge from various data by fine-tuning on different auxiliary
tasks. However, how to properly select training data and fine-tuning tasks is
still an open problem. In this paper, we tackle the problem by transferring
knowledge from three aspects, i.e., domain, language and task, and
strengthening connections among them. Specifically, we propose four practical
guidelines to guide knowledge transfer and task fine-tuning. Based on these
guidelines, we design a target-oriented fine-tuning (TOF) framework to exploit
various data from three aspects in a unified training manner. Experimental
results on six benchmarks show that our method yields consistent improvements
over baselines in both cross-domain and cross-lingual scenarios. Particularly,
we achieve new state-of-the-art performance on five benchmarks.

本文提出了一个以目标为导向的微调框架，从特定领域、语言和任务等三个方面利用各种数据来解决零资源命名实体识别中的数据稀缺问题，并在六个基准测试中实现了新的最先进性能表现。

面向目标的零资源命名实体识别微调

Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition

Transfer learning has yielded state-of-the-art (SoTA) results in many
supervised NLP tasks. However, annotated data for every target task in every
target language is rare, especially for low-resource languages. We propose
UXLA, a novel unsupervised data augmentation framework for zero-resource
transfer learning scenarios. In particular, UXLA aims to solve cross-lingual
adaptation problems from a source language task distribution to an unknown
target language task distribution, assuming no training label in the target
language. At its core, UXLA performs simultaneous self-training with data
augmentation and unsupervised sample selection. To show its effectiveness, we
conduct extensive experiments on three diverse zero-resource cross-lingual
transfer tasks. UXLA achieves SoTA results in all the tasks, outperforming the
baselines by a good margin. With an in-depth framework dissection, we
demonstrate the cumulative contributions of different components to its
success.

提出了 UXLA，一种新的无监督数据增强框架，用于零资源转移学习场景中的跨语言适应问题。UXLA 在三个不同的零资源跨语言转移任务上取得了 SOTA 结果，胜过基准线很多。

UXLA：零资源跨语言自然语言处理的强大无监督数据增强框架

UXLA: A Robust Unsupervised Data Augmentation Framework for  Zero-Resource Cross-Lingual NLP

The neural machine translation model has suffered from the lack of
large-scale parallel corpora. In contrast, we humans can learn multi-lingual
translations even without parallel texts by referring our languages to the
external world. To mimic such human learning behavior, we employ images as
pivots to enable zero-resource translation learning. However, a picture tells a
thousand words, which makes multi-lingual sentences pivoted by the same image
noisy as mutual translations and thus hinders the translation model learning.
In this work, we propose a progressive learning approach for image-pivoted
zero-resource machine translation. Since words are less diverse when grounded
in the image, we first learn word-level translation with image pivots, and then
progress to learn the sentence-level translation by utilizing the learned word
translation to suppress noises in image-pivoted multi-lingual sentences.
Experimental results on two widely used image-pivot translation datasets,
IAPR-TC12 and Multi30k, show that the proposed approach significantly
outperforms other state-of-the-art methods.

本文提出了一种基于图片引导的无资源机器翻译渐进式学习方法，从词级别开始逐步学习，最终将学习到的词汇级别的翻译应用在句子级别的翻译上，从而有效减少了图片噪声对模型学习的干扰，在两个广泛使用的基于图像引导的翻译数据集上，该方法明显优于其他最先进的方法。