Combating Adversarial Misspellings with Robust Word Recognition
Table 2: Word Error Rates (WER) of ScRNN with each backoff strategy, plus ATD and an ScRNN trained only on the background corpus (78K vocabulary) The error rates include 5.25% OOV words.
['[BOLD] Word Recognition Spell-Corrector', '[BOLD] Word Recognition  [BOLD] Swap', '[BOLD] Word Recognition  [BOLD] Drop', '[BOLD] Word Recognition  [BOLD] Add', '[BOLD] Word Recognition  [BOLD] Key', '[BOLD] Word Recognition  [BOLD] All']
Note that ATD incorrectly predicts 11.2 words for every 100 words (in the ‘all’ setting), whereas, all of the backoff variations of the ScRNN reconstruct better. The most accurate variant involves backing off to the background model, resulting in a low error rate of 6.9%, leading to the best performance on word recognition. This is a 32% relative error reduction compared to the vanilla ScRNN model with a pass-through backoff strategy. We can attribute the improved performance to the fact that there are 5.25% words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus. Notably, only training on the larger background corpus does worse, at 8.7%, since the distribution of word frequencies is different in the background corpus compared to the foreground corpus.