Although the E2E dataset contains a large number of samples, each MR is associated on average with 8.65 different reference utterances, effectively offering less than 5K unique MRs in the training set ( Fig.
in order to bring our model to produce more sophisticated utterances, we experimented with filtering the training data to contain only the most natural sounding and structurally complex utterances for each MR. For instance, we prefer having an elegant, singlesentence utterance with an apposition as the reference for an MR, rather than an utterance composed of three simple sentences, two of which begin with "it" (see the examples in Table 5).