Learning Numeral Embedding
Table 4: The results of the numeral prediction tasks.
['[EMPTY]', '[BOLD] Wikipedia-1B, dim 300 \\mathbf{S_{A}}', '[BOLD] Wikipedia-1B, dim 300 \\mathbf{S_{A}}', '[BOLD] Wikipedia-1B, dim 300 \\mathbf{S_{A}}', '[BOLD] Wikipedia-1B, dim 300 \\mathbf{S_{B}}', '[BOLD] Wikipedia-1B, dim 300 \\mathbf{S_{B}}', '[BOLD] Wikipedia-1B, dim 300 \\mathbf{S_{B}}', '[BOLD] Numeracy-600k, dim 300 \\mathbf{S_{A}}', '[BOLD] Numeracy-600k, dim 300 \\mathbf{S_{A}}', '[BOLD] Numeracy-600k, dim 300 \\mathbf{S_{A}}', '[BOLD] Numeracy-600k, dim 300 \\mathbf{S_{B}}', '[BOLD] Numeracy-600k, dim 300 \\mathbf{S_{B}}', '[BOLD] Numeracy-600k, dim 300 \\mathbf{S_{B}}']
Our methods significantly outperform the NumAsTok and Fixed baselines on all the three metrics. D-LSTM also performs well but needs more parameters and computing time than our methods. We split the dataset to 450k sentences for training, 50k for validation and 100k for testing. We use micro-F1 and macro-F1 in addition to AVGR as the evaluation metrics. The result shows that our methods achieve much better performance compared to the baselines.