Table 7 (Seq2seq(6)) includes performances significantly lower than the other Att-RNN and Seq2seq models due to a strong tendency towards none intent prediction with ESLO2 data as part of the training set (Figure 3 Seq2seq(6)) (Desot et al., 2019a;Desot et al., 2019b).
these systems include multiple modules, such as a Spoken Language Understanding (SLU) module that must be able to extract the intent of the user from the voice command and its named entities.