  Defining words in a textual context is a useful task both for practical
purposes and for gaining insight into distributed word representations.
Building on the distributional hypothesis, we argue here that the most natural
formalization of definition modeling is to treat it as a sequence-to-sequence
task, rather than a word-to-sequence task: given an input sequence with a
highlighted word, generate a contextually appropriate definition for it. We
implement this approach in a Transformer-based sequence-to-sequence model. Our
proposal allows to train contextualization and definition generation in an
end-to-end fashion, which is a conceptual improvement over earlier works. We
achieve state-of-the-art results both in contextual and non-contextual
definition modeling.
