  Homographs, words with the same spelling but different meanings, remain
challenging in Neural Machine Translation (NMT). While recent works leverage
various word embedding approaches to differentiate word sense in NMT, they do
not focus on the pivotal components in resolving ambiguities of homographs in
NMT: the hidden states of an encoder. In this paper, we propose a novel
approach to tackle homographic issues of NMT in the latent space. We first
train an encoder (aka "HDR-encoder") to learn universal sentence
representations in a natural language inference (NLI) task. We further
fine-tune the encoder using homograph-based synset sentences from WordNet,
enabling it to learn word-level homographic disambiguation representations
(HDR). The pre-trained HDR-encoder is subsequently integrated with a
transformer-based NMT in various schemes to improve translation accuracy.
Experiments on four translation directions demonstrate the effectiveness of the
proposed method in enhancing the performance of NMT systems in the BLEU scores
(up to +2.3 compared to a solid baseline). The effects can be verified by other
metrics (F1, precision, and recall) of translation accuracy in an additional
disambiguation task. Visualization methods like heatmaps, T-SNE and translation
examples are also utilized to demonstrate the effects of the proposed method.
