Distributed representations of words which map each word to a continuous
vector have proven useful in capturing important linguistic information not
only in a single language but also across different languages. Current
unsupervised adversarial approaches show that it is possible to build a mapping
matrix that align two sets of monolingual word embeddings together without high
quality parallel data such as a dictionary or a sentence-aligned corpus.
However, without post refinement, the performance of these methods' preliminary
mapping is not good, leading to poor performance for typologically distant
languages.
In this paper, we propose a weakly-supervised adversarial training method to
overcome this limitation, based on the intuition that mapping across languages
is better done at the concept level than at the word level. We propose a
concept-based adversarial training method which for most languages improves the
performance of previous unsupervised adversarial methods, especially for
typologically distant language pairs.

本论文提出了一种基于概念的弱监督对抗训练方法，通过将单词映射到连续向量的分布表示法，实现不需要大量平行数据（如字典或句子对齐语料库）的语言间互译，并针对语言间距离大的情况，在大多数语言上改善了以前无监督的对抗方法的性能。

基于概念的弱监督对抗学习用于跨语言词嵌入

Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual  Word Embeddings

Interpretability has become an important topic of research as more machine
learning (ML) models are deployed and widely used to make important decisions.
Most of the current explanation methods provide explanations through feature
importance scores, which identify features that are important for each
individual input. However, how to systematically summarize and interpret such
per sample feature importance scores itself is challenging. In this work, we
propose principles and desiderata for \emph{concept} based explanation, which
goes beyond per-sample features to identify higher-level human-understandable
concepts that apply across the entire dataset. We develop a new algorithm, ACE,
to automatically extract visual concepts. Our systematic experiments
demonstrate that \alg discovers concepts that are human-meaningful, coherent
and important for the neural network's predictions.

提出了概念解释的原则和求解算法 ACE，能够系统性地通过可视化概念提供对深度神经网络预测的重要性的人类可解释性解释。