  One of possible ways of obtaining continuous-space sentence representations
is by training neural machine translation (NMT) systems. The recent attention
mechanism however removes the single point in the neural network from which the
source sentence representation can be extracted. We propose several variations
of the attentive NMT architecture bringing this meeting point back. Empirical
evaluation suggests that the better the translation quality, the worse the
learned sentence representations serve in a wide range of classification and
similarity tasks.
