Recurrent Neural Networks (RNN) can deal with (textual) input with various length and hence have a lot of applications in software systems and software engineering applications. RNNs depend on word embeddings that are usually pre-trained by third parties to encode textual inputs to numerical values. It is well known that problematic word embeddings can lead to low model accuracy. In this paper, we propose a new technique to automatically diag- nose how problematic embeddings impact model performance, by comparing model execution traces from correctly and incorrectly executed samples. We then leverage the diagnosis results as guid- ance to harden/repair the embeddings. Our experiments show that TRADER can consistently and effectively improve accuracy for real world models and datasets by 5.37% on average, which represents substantial improvement in the literature of RNN models.