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One central mystery of neural NLP is what neural models "know" about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more thandoi:10.18653/v1/d17-1268 dblp:conf/emnlp/MalaviyaNL17 fatcat:ucmtp53ksze4jeoofa3rpcquui