As far as possible we try to perform an appleto-apple comparison on these tasks, and our goal is an analytic one-to investigate how well semantic information can be encoded in multi-sense embeddings and how they can improve NLU performances-rather than an attempt to create state-of-the-art results.
for example, in tagging tasks (e.g., NER, POS), we follow the protocols in (Collobert et al., 2011) using the concatenation of neighboring embeddings as input features rather than treating embeddings as auxiliary features which are fed into a CRF model along with other manually developed features as in Pennington et al.