TF-IDF Features vs Embedding Layer
It is common for TFIDF to be a strong model. People constantly get high places in Kaggle competitions with TFIDF models. Here is a link to the winning solution that used TFIDF as one of its features (1st place Otto product classification). You will most likely get a stronger model if you combine the TFIDF and RNN into one ensemble. Other results from Kaggle:


2nd place: https://www.kaggle.com/c/stumbleupon/discussion/6184
4th place: https://www.kaggle.com/c/avito-demand-prediction/discussion/59881
3rd place: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/52762


https://www.kaggle.com/c/avito-demand-prediction/discussion/56897:


  A good number of kernels are going the traditional route with
  CountVectorizer/TF-IDF, and some brave souls (I say brave because
  training is slower and the results don't seem as spectacular so far)
  have been experimenting with embeddings, as per the previous
  competitions.