Embodiments provide methods and apparatus for improving responses of automated conversational agents. The method includes generating a vector representation of a conversational input provided by a user. The vector representation is used to determine an intent of the conversational input. Further, annotators generate bait sentences that cover multiple aspects of the intent. Then, sentences in a data pool are accessed. The bait sentences and the data pool sentences are converted into a first and a second set of vector representations, respectively. The first and the second set of vector representations are compared to retrieve a list of similar sentences. The list of similar sentences includes one or more sentences of the data pool that are semantically similar to the bait sentences. The list of similar sentences is analyzed for updating the intent data and thereby improve the responses.