Unifying Bayesian Inference and Vector Space Models for Improved Decipherment
Qing Doua, Ashish Vaswania b, Kevin K. Knighta, Chris Dyerc
- aInformation Sciences Institute
- bDepartment of Computer Science University of Southern California
- cSchool of Computer Science Carnegie Mellon University
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Abstract
We introduce into Bayesian decipherment a base distribution derived from similarities of word embeddings. We use Dirichlet multinomial regression (Mimno and McCallum, 2012) to learn a mapping between ciphertext and plaintext word embeddings from non-parallel data. Experimental results show that the base distribution is highly beneficial to decipherment, improving state-of-the-art decipherment accuracy from 45.8% to 67.4% for Spanish/English, and from 5.1% to 11.2% for Malagasy/English.
TL;DRExperimental results show that the base distribution is highly beneficial to decipherment, improving state-of-the-art decipherment accuracy from 45.8% to 67.4% for Spanish/English, and from 5.1% to 11.2% for Malagasy/English. (Semantic Scholar)