(2017b) proposed a self-attention layer on top of the concatenated bidirectional LSTM hidden states for WSD and introduced multi-task learning with part-ofspeech tagging and semantic labeling as auxiliary tasks.
on average across the test sets, their approach did not outperform SVM with word embedding features.