TextNAS: A Neural Architecture Search Space tailored for Text Representation
Table 4: Test accuracy on the text classification datasets. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level.
['Model', 'AG', 'Sogou', 'DBP', 'Yelp-B', 'Yelp', 'Yahoo', 'Amz', 'Amz-B']
The results demonstrate that the TextNAS model outperforms state-of-the-art methods on all text classification datasets except Sogou. One potential reason is that Sogou is a dataset in Chinese language, while the Glove embedding vectors are trained by English corpus. One can improve the performance by adding Chinese-language embeddings or char-embeddings, but we do not add them to keep the solution neat. In addition, we can pay a specific attention to the comparison of TextNAS with 29-layers CNN (Conneau ET AL., 2016) and 24-layers Transformer (VASWANI ET AL., 2017). As shown in the table, the TextNAS network improves two baselines by a large margin, indicating the advantage for mixture of different layers.