We hypothesize that mixing the sequence x with x changes the context for all tokens and injects too much noise, hence making learning the labels for the tokens challenging.
the relative distance between x and x in the manifold mapped by neural networks is further in sequence labeling than sentence classification (demonstrated in Figure 2), which is intuitively understandable as every data point in sentence classification is the pooling over all the tokens in one sentence while every token is a single data point in sequence labeling.