Adapting Sequence to Sequence models for Text Normalization in Social Media
Table 5: Most frequent correct (left table) and incorrect (right table) normalizations of our word-level Seq2Seq model. We present how many times a source tweet was (in)correctly normalized (Count column) as well as how many times that term appears in the source-side of the examples (Source). For cases where a token can be normalized to itself, we include how many times that term appears unchanged (information in parentheses)
['Source u', 'Target {you’re, you, u, your}', 'Count 234', 'Source 335 (2)']
We perform an extensive error analysis. First, we check the model output, particularly in which cases our model fails. Note that we also keep track of how many times a term remains unchanged, specifically for cases where that term has multiple mappings available We can see that our model can handle multiple mappings when those are adequately represented in the training data. Most of the incorrect normalizations appear less than 50 times in our data (very infrequent) or are ambiguously normalized in some examples and left intact in other examples, e.g. “rt” and “2” remain unchanged a few times, and these are the cases that the model missed. These types of errors can be handled by adding more training data. Furthermore, we should note that our model can also be adjusted to work with distantly supervised data or with ensemble methods.