Learning Numeral Embedding
Table 10: Probing test results. MLP1 and MLP2 denote MLP with one and two hidden layers respectively.
['[EMPTY]', 'Decoding MLP1', 'Decoding MLP2', 'Subtraction BiLinear', 'Subtraction BiLinear+MLP1', 'Subtraction MLP1', 'Subtraction MLP2']
For Decoding, the dataset can be directly used. For Subtraction, we randomly sample 10^{5} pairs of numerals (n_{1},n_{2}) from the dataset and assign n_{1}-n_{2} as the prediction target. Following Wallace2019DoNM, we randomly split 80\% of each dataset for training and 20\% for testing. We use SGD to optimize the mean square error (MSE) loss. The results show that our methods have better performance than the baselines overall.