AbstractThis paper presents the GX system for the Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) task. The purpose of the MCL-WiC task is to tackle the challenge of capturing the polysemous nature of words without relying on a fixed sense inventory in a multilingual and cross-lingual setting. To solve the problems, we use context-specific word embeddings from BERT to eliminate the ambiguity between words in different contexts. For languages without an available training corpus, such as Chinese, we use neuron machine translation model to translate the English data released by the organizers to obtain available pseudo-data. In this paper, we apply our system to the English and Chinese multilingual setting and the experimental results show that our method has certain advantages.