  This paper explores negative lexical constraining in English to Czech neural
machine translation. Negative lexical constraining is used to prohibit certain
words or expressions in the translation produced by the neural translation
model. We compared various methods based on modifying either the decoding
process or the training data. The comparison was performed on two tasks:
paraphrasing and feedback-based translation refinement. We also studied to
which extent these methods "evade" the constraints presented to the model
(usually in the dictionary form) by generating a different surface form of a
given constraint.We propose a way to mitigate the issue through training with
stemmed negative constraints to counter the model's ability to induce a variety
of the surface forms of a word that can result in bypassing the constraint. We
demonstrate that our method improves the constraining, although the problem
still persists in many cases.
