Abstract: One embodiment of the present invention sets forth a technique for analyzing a transcript of a structured interaction. The technique includes determining a first portion of the transcript that corresponds to a first dialogue act. The technique also includes matching the first portion of the transcript to a first component of a script for the structured interaction based on a first set of embeddings for the first portion of the transcript and a second set of embeddings associated with the first component of the script. The technique further includes causing a first mapping between the first portion of the transcript and the first component to be outputted.

Abstract: A computer-implemented method according to one embodiment includes identifying features of a plurality of data instances within a target domain; assigning weights to the plurality of data instances within the target domain, based on similarities among the features; selecting a subset of the plurality of data instances within the target domain, based on the weights; associating expert annotations with respective ones of data instances within the subset; and training a machine learning algorithm, utilizing the subset of the plurality of data instances and associated expert annotations.

Abstract: A computer-implemented method according to one embodiment includes identifying features of a plurality of data instances within a target domain; assigning weights to the plurality of data instances within the target domain, based on similarities among the features; selecting a subset of the plurality of data instances within the target domain, based on the weights; associating expert annotations with respective ones of data instances within the subset; and training a machine learning algorithm, utilizing the subset of the plurality of data instances and associated expert annotations.

Abstract: In order to address annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system determines an annotation probability distribution based on a factor-graph model of the batch annotations. In particular, during operation the system computes the factor-graph model that represents relationships between feature vectors that represent the comments and the annotations for the comments. Note that, for a given batch of k comments, the factor-graph model may include a statistically dependent combination of statistically independent models of the interrelationships between the feature vectors and the annotations for the k comments. Then, the system calculates the annotation probability distribution based on model parameters associated with the factor-graph model, a mapping function that maps from the feature vectors to the annotations, and an indicator function that represents the annotations for the comments in the batches.

Abstract: In order to leverage annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system may select a subset of the comments for annotation based on how informative expected annotations for the comments in the subset are for the one or more classifiers and probabilities of occurrence of the expected annotations based on a predetermined annotation probability distribution. Note that the classifier may predict how likely the expected annotations are accurate for the comments in a given subset. Moreover, the predetermined annotation probability distribution may specify the annotation bias. In this way, the system may use the annotation bias to select the subset that is likely to receive expected annotations and, thus, are that are easier to use in training the classifier.

Abstract: In order to address annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system determines an annotation probability distribution based on a factor-graph model of the batch annotations. In particular, during operation the system computes the factor-graph model that represents relationships between feature vectors that represent the comments and the annotations for the comments. Note that, for a given batch of k comments, the factor-graph model may include a statistically dependent combination of statistically independent models of the interrelationships between the feature vectors and the annotations for the k comments. Then, the system calculates the annotation probability distribution based on model parameters associated with the factor-graph model, a mapping function that maps from the feature vectors to the annotations, and an indicator function that represents the annotations for the comments in the batches.

Abstract: In order to leverage annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system may select a subset of the comments for annotation based on how informative expected annotations for the comments in the subset are for the one or more classifiers and probabilities of occurrence of the expected annotations based on a predetermined annotation probability distribution. Note that the classifier may predict how likely the expected annotations are accurate for the comments in a given subset. Moreover, the predetermined annotation probability distribution may specify the annotation bias. In this way, the system may use the annotation bias to select the subset that is likely to receive expected annotations and, thus, are that are easier to use in training the classifier.