Noise-robust Named Entity Understanding for Virtual Assistants
Table 2: NER experiment with self-attention and residual connection, showing relative change in % from 10 runs average.
['[EMPTY]', '[BOLD] + self-attention  [BOLD] Top-1 F1', '[BOLD] + self-attention  [BOLD] Top-5 F1', '[BOLD] + residual connection  [BOLD] Top-1 F1', '[BOLD] + residual connection  [BOLD] Top-5 F1', '[BOLD] + both  [BOLD] Top-1 F1', '[BOLD] + both  [BOLD] Top-5 F1']
Both of these encoding mechanisms individually provide gains to the model, and adding them together leads to a greater cumulative accuracy boost. The results of addition suggest that the two encoding strategies are specializing on different types of queries. We also observe larger gains on the subset of noisy mentions, in particular for Top-1 F1 score, which infers the enhanced encoding architecture can better represent more complicated dependencies among tokens, or memorize noisy entity mentions better.