Semantic Hilbert Space for Text Representation Learning
Table 4. Ablation Test
['Setting', 'SST', 'Δ']
Due to limited space, we only reported the ablation test result for SST, which is the largest and hence the most representative dataset. We used 100-dimensional real-valued word vectors and 50-dimensional complex-valued vectors for the models in the ablation test. All models under ablation were comparable in terms of time cost. Tab. In particular, replacing complex embedding with double-dimension real word embedding led to a 5% drop in performance, which indicates that the complex-valued word embedding was not merely doubling the number of parameters.