In the context of WSD framed as a sequence learning problem, a sequence-to-sequence model takes as input a training set of labeled sequences (cf.
section 3.1) and learns to replicate an input sequence x while replacing each content word with its most suitable word sense from s. sequence-to-sequence WsD can be viewed as the combination of two sub-tasks: • A memorization task, where the model learns to replicate the input sequence token by token at decoding time; • The actual disambiguation task where the model learns to replace content words across the input sequence with their most suitable senses from the sense inventory s. In the latter stage, multi-word expressions (such as nominal entity mentions or phrasal verbs) are replaced by their sense identifiers, hence yielding an output sequence that might have a different length than x.