Multidirectional Associative Optimization of Function-Specific Word Representations
Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation.
['[BOLD] Setup', '[BOLD] Setup', '[BOLD] Baselines', '[BOLD] Baselines', '[BOLD] Ours SVO', '[BOLD] Ours SV-VO']
Thematic-Fit Evaluation. (G15), and 2) current state-of-the-art tensor-based neural model by Tilk et al. (TK16). We find that vectors taken from the model trained in the joint 3-group SVO setup perform on a par with state-of-the-art models also in the 2-group evaluation on SV and VO subsets. Vectors trained explicitly in the 2-group setup using three times more data lead to substantial improvements on PADO414. As a general finding, our function-specific approach leads to peak performance on both data sets. The results are similar with 25-dim SVO vectors. We reach the peak scores on all evaluation sets using the sync+shared variant. We suspect that asynchronous training deteriorates performance because each sub-network overwrites the updates of other sub-networks as their training is not tied through a joint loss function. On the other hand, the synchronous training regime guides the model towards making updates that can benefit all sub-networks.