Misspelling Oblivious Word Embeddings
Table 2: Performance on POS tagging task for UPOS tags using CRF. The models were trained on 100 epochs with an early stop (small difference on validation error) mechanism enabled. Considering F1 score, we evaluate on 2 variants of test data: Original (correctly spelled) on the right hand side of the table and 100% misspelled on the left hand side.
['Test Data Training Data', '100% Misspelled Original', '100% Misspelled 100% Miss.', '100% Misspelled 10% Miss.', 'Original', 'Original 100% Miss.', 'Original 10% Miss.']
To assess the impact of misspellings we artificially inject misspellings in the dataset. We train MOE on three different dataset variants: a non-misspelled dataset, to verify that MOE does not jeopardize the performance on correct words; a dataset where 10% of words contain a misspelling, to simulate a realistic environment where some of the words are misspelled; and finally on a dataset where 100% of words contain misspellings, to simulate a highly distorted environment. This model adds a dependency on both layers of a Bi-LSTM component to the tag variables in the CRF. We evaluate the F1 score of the system for the three dataset variants we describe above. We test two different representations as input to the CRF: FastText (our baseline), and MOE embeddings.