neuralVietNLP: neural NLP Tools for Vietnamese Language Processing
Bang 5: P-values of the paired t-tests between embeddings obtained using dpUGC at different learning step (Emb@L). “-” denotes values of these entries in the upper triangular matrix are the values of the transposed entries in the lower triangular matrix. P-values in bold font are statistical significance at the level of 0.05.
['Emb@L', '20', '200', '500', '1000', '5000', '10000', '20000', '50000', '90K', '100K']
Similar to \newciteVu: (Emb@1000) seems to be a good word embbedding candidate to have a good trade-off between privacy guarantee and data utility. Emb@1000 was in favor because of two reasons. Firstly, in training privacy-guaranteed embeddings, we try to stop as early as possible since the more training steps we run, the higher privacy we have to sacrifice Vu et al. Secondly, its performance in the Word Analogy Task was more or less similar to the other good embedding at the learning step 90K (i.e., Emb@90K). Therefore, selecting the pre-trained embedding at the learning step 1000 is the best option for privacy-guaranteed embedding using dpUGC. In summary, in this task, we showed how ETNLP can be used to select a good word embedding candidate for privacy-guaranteed knowledge sharing. Normally, this selection process is very time consuming, however, it is much easier with ETNLP since it allows users to import multiple embeddings for running evaluations.