The large size of D S is not a problem: we use an inverted index that we probe with documents from D T to efficiently identify D S .
yet another option is to learn projections to align generic embeddings to the target sense (Bollegala et al., 2015;Barnes et al., 2018;K Sarma et al., 2018), or to a shared common space (yin and Schütze, 2016;Coates and Bollegala, 2018;Bollegala and Bao, 2018) in carefully controlled experiments, none of the proposed approaches to adapting pretrained embeddings consistently beats the trivial baseline of discarding them and training afresh on D T !