A method for language understanding, comprising: receiving (304, 804) an input utterance; ascertaining a likelihood that the input utterance is part of an existing dialog by considering one or more factors being at least one of i) a length of time from a prior utterance and/or prompt, ii) whether the utterance has been received in response to a prompt that is part of an existing dialog, iii) whether the utterance is in response to any prompt or not, or iv) whether a prior dialog has been closed, the factors being considered either as a set of rules or as inputs into a machine learning or other model that combines the factors into a probability, or some combination thereof; determining whether the input utterance is part of an existing dialog or not by comparing the likelihood to a threshold; responsive to determining that the input utterance is part of an existing dialog: identifying (310, 806) prior dialog context information that was received in conjunction with the input utterance; selecting a first trained language understanding model, wherein the first trained language understanding model is trained using example utterances and associated prior dialog context; determining a first intent from the input utterance and the prior dialog context information using the first trained language understanding model; and returning the first intent; and responsive to determining that the input utterance is part of a new dialog: selecting a second trained language understanding model, wherein the second trained language understanding model is trained using example utterances without associated prior dialog context pairs; determining a second intent from the input utterance using the second trained language understanding model; and returning the second intent. receiving (304, 804) an input utterance; ascertaining a likelihood that the input utterance is part of an existing dialog by considering one or more factors being at least one of i) a length of time from a prior utterance and/or prompt, ii) whether the utterance has been received in response to a prompt that is part of an existing dialog, iii) whether the utterance is in response to any prompt or not, or iv) whether a prior dialog has been closed, the factors being considered either as a set of rules or as inputs into a machine learning or other model that combines the factors into a probability, or some combination thereof; determining whether the input utterance is part of an existing dialog or not by comparing the likelihood to a threshold; responsive to determining that the input utterance is part of an existing dialog: identifying (310, 806) prior dialog context information that was received in conjunction with the input utterance; selecting a first trained language understanding model, wherein the first trained language understanding model is trained using example utterances and associated prior dialog context; determining a first intent from the input utterance and the prior dialog context information using the first trained language understanding model; and returning the first intent; and identifying (310, 806) prior dialog context information that was received in conjunction with the input utterance; selecting a first trained language understanding model, wherein the first trained language understanding model is trained using example utterances and associated prior dialog context; determining a first intent from the input utterance and the prior dialog context information using the first trained language understanding model; and returning the first intent; and responsive to determining that the input utterance is part of a new dialog: selecting a second trained language understanding model, wherein the second trained language understanding model is trained using example utterances without associated prior dialog context pairs; determining a second intent from the input utterance using the second trained language understanding model; and returning the second intent. selecting a second trained language understanding model, wherein the second trained language understanding model is trained using example utterances without associated prior dialog context pairs; determining a second intent from the input utterance using the second trained language understanding model; and returning the second intent.