A method for a conversational pipeline, the method comprising: generating an initial training dataset comprising a first plurality of synthetic training data items, wherein a synthetic training data item of the first plurality of synthetic training data items comprises a) a textual representation of a synthetic sentence and b) one or more transcodes of the synthetic sentence comprising one or more actions and one or more entities associated with the one or more actions included in the synthetic sentence; for each synthetic training data item of the first plurality of synthetic training data items, converting the textual representation of the synthetic sentence of the synthetic training data item into a sequence of phonemes that represent the synthetic sentence to determine a first modified training dataset comprising a second plurality of synthetic training data items, wherein a synthetic training data item of the second plurality of synthetic training data items comprises a) sequences phonemes that represent synthetic sentences and b) the transcodes of the synthetic sentences; and training a first machine learning model as a transcoder that determines transcodes comprising actions and associated entities from sequences of phonemes, wherein the training is performed using the first modified training dataset; receiving a second training dataset comprising a plurality of data items, wherein each data item of the plurality of data items comprises acoustic features derived from audio data for an utterance and a textual representation of the utterance; for each data item of the plurality of data items, converting the textual representation of the utterance into a sequence of phonemes that represent the utterance to determine a second modified training dataset comprising a plurality of modified data items that comprise a) acoustic features of audio data for utterances and b) sequences of phonemes that represent the utterances; and training a second machine learning model as an acoustic model that generates sequences of phonemes from acoustic features derived from audio data of utterances, wherein the training is performed using the second modified second training dataset. generating an initial training dataset comprising a first plurality of synthetic training data items, wherein a synthetic training data item of the first plurality of synthetic training data items comprises a) a textual representation of a synthetic sentence and b) one or more transcodes of the synthetic sentence comprising one or more actions and one or more entities associated with the one or more actions included in the synthetic sentence; for each synthetic training data item of the first plurality of synthetic training data items, converting the textual representation of the synthetic sentence of the synthetic training data item into a sequence of phonemes that represent the synthetic sentence to determine a first modified training dataset comprising a second plurality of synthetic training data items, wherein a synthetic training data item of the second plurality of synthetic training data items comprises a) sequences phonemes that represent synthetic sentences and b) the transcodes of the synthetic sentences; and training a first machine learning model as a transcoder that determines transcodes comprising actions and associated entities from sequences of phonemes, wherein the training is performed using the first modified training dataset; receiving a second training dataset comprising a plurality of data items, wherein each data item of the plurality of data items comprises acoustic features derived from audio data for an utterance and a textual representation of the utterance; for each data item of the plurality of data items, converting the textual representation of the utterance into a sequence of phonemes that represent the utterance to determine a second modified training dataset comprising a plurality of modified data items that comprise a) acoustic features of audio data for utterances and b) sequences of phonemes that represent the utterances; and training a second machine learning model as an acoustic model that generates sequences of phonemes from acoustic features derived from audio data of utterances, wherein the training is performed using the second modified second training dataset.