12. A method comprising: obtaining, during operation of a computer-implemented dialogue system comprising a natural language understanding engine, data identifying (i) a first input conversational turn that was provided as input to the natural language understanding engine during a dialogue between a user and the computer-implemented dialogue system and (ii) a first annotation of the first input conversational turn generated by the natural language understanding engine, wherein the natural language understanding engine has been trained on a first set of training data comprising a plurality of training conversational turns; determining that the first annotation accurately characterized the first input conversational turn; determining, based on the training conversational turns in the first set of training data, that the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn; in response to determining that (i) the first annotation accurately characterized the first input conversational turn but (ii) the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn: obtaining one or more first paraphrases of the first input conversational turn; and generating, for each of the one or more first paraphrases, a respective first training example that identifies the first annotation as the correct annotation for the first paraphrase; and training the natural language understanding engine on at least the first training examples.
12. A method comprising: obtaining, during operation of a computer-implemented dialogue system comprising a natural language understanding engine, data identifying (i) a first input conversational turn that was provided as input to the natural language understanding engine during a dialogue between a user and the computer-implemented dialogue system and (ii) a first annotation of the first input conversational turn generated by the natural language understanding engine, wherein the natural language understanding engine has been trained on a first set of training data comprising a plurality of training conversational turns; determining that the first annotation accurately characterized the first input conversational turn; determining, based on the training conversational turns in the first set of training data, that the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn; in response to determining that (i) the first annotation accurately characterized the first input conversational turn but (ii) the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn: obtaining one or more first paraphrases of the first input conversational turn; and generating, for each of the one or more first paraphrases, a respective first training example that identifies the first annotation as the correct annotation for the first paraphrase; and training the natural language understanding engine on at least the first training examples. 18. The method of claim 12 , wherein determining that the first annotation accurately characterized the first input conversational turn comprises: processing the first input conversational turn and the first annotation using a post-hoc annotation machine learning model that is configured to generate as output a quality score that represents a likelihood that the first annotation is an accurate characterization of the first input conversational turn; and determining that the quality score exceeds a threshold score.