Binary Paragraph Vectors
Table 2: Information retrieval results for 32-bit binary codes constructed by first inferring 32d real-valued paragraph vectors and then employing a separate hashing algorithm for binarization. Paragraph vectors were inferred using PV-DBOW with bigrams.
['Hashing algorithm', '20 Newsgroups MAP', '20 Newsgroups NDCG@10', 'RCV1 MAP', 'RCV1 NDCG@10', 'English Wikipedia MAP', 'English Wikipedia NDCG@10']
We also compared binary paragraph vectors against codes constructed by first inferring short, real-valued paragraph vectors and then using a separate hashing algorithm for binarization. When the dimensionality of the paragraph vectors is equal to the size of binary codes, the number of network parameters in this approach is similar to that of Binary PV models. Paragraph vectors in these experiments were inferred using PV-DBOW with bigrams. On the 20 Newsgroups and RCV1 datasets Binary PV-DBOW yielded higher MAP than the two baseline approaches. On English Wikipedia iterative quantization achieved MAP equal to Binary PV-DBOW, while random hyperplane projection yielded lower MAP. Some gain in precision of top hits can be observed for iterative quantization, as indicated by NDCG@10. It is also worth noting that end-to-end inference in Binary PV models is more convenient than inferring real-valued vectors and then using another algorithm for hashing.