Adapting Sequence to Sequence models for Text Normalization in Social Media
Table 3: Comparison of our S2S models with word-level baselines.
['[BOLD] Model name', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1', '[BOLD] Method highlights']
Dict2 shows that handling ambiguous cases inappropriately results in a dramatic drop in precision. It is therefore necessary for a text normalization model to be able to correctly normalize text in the occurrence of multiple mappings. S2SMulti is a baseline method that firstly normalizes terms that have a unique mapping, based on source and target tokens found in the training data, and later on utilizes a word-level Seq2Seq model that is trained to correct one-to-many mappings (an unnormalized token that can be transformed into several standard words, e.g. “ur” → {“you are”, “your”}). We can see that this method has a better performance, which validates our hypothesis that a naive word replacement would not suffice, however the end-to-end Seq2Seq model (S2S) performs better than S2SMulti, i.e. Seq2Seq models can handle both unique and multiple mappings seamlessly without any additional feature engineering effort apart from tokenization. One of our main questions is how crucial contextual information is for text normalization. We test the importance of context by performing two experiments. For our first experiment, we perform the following preprocessing of the training data: we constrain the target-side of each example by replacing each word that remains unchanged (no normalization needed) with a special @self symbol. With this representation all tokens that need normalization are preserved and the model would learn which words remain the same. We call this model S2SSelf. In most cases we observe that our model relies on context to normalize short tokens, while S2SSelf fails to correct such terms.