Diversity by Phonetics and its Application in Neural Machine Translation
Table 2: Density result (Converge threshold: 0.001; 1, 2, 3 nearest neighbour). Smaller values, more distributed words in each cluster.
['Method', 'Max 1', 'Max 2', 'Max 3', 'Sum 1', 'Sum 2', 'Sum 3']
For each point x in the smoothed convex hull of all words/characters, let Di(x) be the distance between x and the i-th nearest neighbor of x in the space X. We then look at either the maximum of Di(x) over all x, or the average. Choosing a larger i captures the ‘density’ of the point-set at larger scales, which is a parameter that can be tuned to be more robust against noise. We numerically integrate the convex hull surface by randomly sampling the points, which are a linear combination of the convex hull corner weighted uniformly at random. Pinyin is the most well-distributed, then random clustering, followed by K-Means.