Abstract
Bilingual word embeddings (BWEs) have proven to be useful in many cross-lingual natural language processing tasks. Previous studies often require bilingual texts or dictionaries that are scarce resources. As a result, in these studies, the exploited explicit semantic information, such as monolingual word co-occurrences and cross-lingual semantic equivalences, is often insufficient for BWE learning, leading to the limitation of learned word representations. To overcome this problem, in this paper, we study how to exploit implicit semantic constraints for better BWEs. Concretely, we first discover implicit monolingual word-level semantic equivalences by pivoting their translations in the other language. Then, we perform BWE learning under various semantic constraints. Experimental results on machine translation and cross-lingual document classification demonstrate the effectiveness of our model.
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