Neural machine translation (NMT) models often suffer from gender biases that
harm users and society at large. In this work, we explore how bridging the gap
between languages for which parallel data is not available affects gender bias
in multilingual NMT, specifically for zero-shot directions. We evaluate
translation between grammatical gender languages which requires preserving the
inherent gender information from the source in the target language. We study
the effect of encouraging language-agnostic hidden representations on models'
ability to preserve gender and compare pivot-based and zero-shot translation
regarding the influence of the bridge language (participating in all language
pairs during training) on gender preservation. We find that language-agnostic
representations mitigate zero-shot models' masculine bias, and with increased
levels of gender inflection in the bridge language, pivoting surpasses
zero-shot translation regarding fairer gender preservation for speaker-related
gender agreement.

探究在多语言 NMT 中，如何通过弥补那些缺少平行数据的语言之间的差距来解决性别偏差问题以及如何通过鼓励语言独立的隐藏表示和比较基于枢轴和零 - shot 翻译模型的影响来达到性别平等.

语言翻译之间的差距如何影响零 - shot 多语言翻译中的性别偏见

Gender Lost In Translation: How Bridging The Gap Between Languages  Affects Gender Bias in Zero-Shot Multilingual Translation

Zero-shot cross-lingual information extraction(IE) aims at constructing an IE
model for some low-resource target languages, given annotations exclusively in
some rich-resource languages. Recent studies based on language-universal
features have shown their effectiveness and are attracting increasing
attention. However, prior work has neither explored the potential of
establishing interactions between language-universal features and contextual
representations nor incorporated features that can effectively model
constituent span attributes and relationships between multiple spans. In this
study, a syntax-augmented hierarchical interactive encoder (SHINE) is proposed
to transfer cross-lingual IE knowledge. The proposed encoder is capable of
interactively capturing complementary information between features and
contextual information, to derive language-agnostic representations for various
IE tasks. Concretely, a multi-level interaction network is designed to
hierarchically interact the complementary information to strengthen domain
adaptability. Besides, in addition to the well-studied syntax features of
part-of-speech and dependency relation, a new syntax feature of constituency
structure is introduced to model the constituent span information which is
crucial for IE. Experiments across seven languages on three IE tasks and four
benchmarks verify the effectiveness and generalization ability of the proposed
method.

本研究提出了一种语法增强的分级交互编码器（SHINE），以转移跨语言 IE 知识，SHINE 能够交互式地捕获特征和文本信息之间的补充信息，以获得不受语言限制的 IE 表示形式，验证了该方法在七种语言上的三个 IE 任务和四个基准上的有效性和泛化能力。

SHINE: 用于零 - shot 跨语言信息提取的句法增强分层交互式编码器

SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot  Cross-lingual Information Extraction

Cross-lingual transfer learning has become an important weapon to battle the
unavailability of annotated resources for low-resource languages. One of the
fundamental techniques to transfer across languages is learning
\emph{language-agnostic} representations, in the form of word embeddings or
contextual encodings. In this work, we propose to leverage unannotated
sentences from auxiliary languages to help learning language-agnostic
representations. Specifically, we explore adversarial training for learning
contextual encoders that produce invariant representations across languages to
facilitate cross-lingual transfer. We conduct experiments on cross-lingual
dependency parsing where we train a dependency parser on a source language and
transfer it to a wide range of target languages. Experiments on 28 target
languages demonstrate that adversarial training significantly improves the
overall transfer performances under several different settings. We conduct a
careful analysis to evaluate the language-agnostic representations resulted
from adversarial training.

本文中提出采用对抗性训练从辅助语言中提取无标注句子帮助学习跨语言不变表示，进而用于跨语言转移，通过实验证明，对抗性训练能有效提高依存句法分析的跨语言性能。