Semantic specialization is the process of fine-tuning pre-trained
distributional word vectors using external lexical knowledge (e.g., WordNet) to
accentuate a particular semantic relation in the specialized vector space.
While post-processing specialization methods are applicable to arbitrary
distributional vectors, they are limited to updating only the vectors of words
occurring in external lexicons (i.e., seen words), leaving the vectors of all
other words unchanged. We propose a novel approach to specializing the full
distributional vocabulary. Our adversarial post-specialization method
propagates the external lexical knowledge to the full distributional space. We
exploit words seen in the resources as training examples for learning a global
specialization function. This function is learned by combining a standard
L2-distance loss with an adversarial loss: the adversarial component produces
more realistic output vectors. We show the effectiveness and robustness of the
proposed method across three languages and on three tasks: word similarity,
dialog state tracking, and lexical simplification. We report consistent
improvements over distributional word vectors and vectors specialized by other
state-of-the-art specialization frameworks. Finally, we also propose a
cross-lingual transfer method for zero-shot specialization which successfully
specializes a full target distributional space without any lexical knowledge in
the target language and without any bilingual data.

本文提出了一种基于对抗训练的后加工特化方法，通过整合外部词汇知识到整个分布式语义空间中，利用资源中出现的词作为训练样例，结合标准 L2 距离和对抗损失，学习全局特化函数，表现良好并提高了词相似度、对话状态跟踪和词汇简化等任务的效果，并提出了一种零射击专业化的跨语言转移方法。