  Large-scale conversational systems typically rely on a skill-routing
component to route a user request to an appropriate skill and interpretation to
serve the request. In such system, the agent is responsible for serving
thousands of skills and interpretations which create a long-tail distribution
due to the natural frequency of requests. For example, the samples related to
play music might be a thousand times more frequent than those asking for
theatre show times. Moreover, inputs used for ML-based skill routing are often
a heterogeneous mix of strings, embedding vectors, categorical and scalar
features which makes employing augmentation-based long-tail learning approaches
challenging. To improve the skill-routing robustness, we propose an
augmentation of heterogeneous skill-routing data and training targeted for
robust operation in long-tail data regimes. We explore a variety of conditional
encoder-decoder generative frameworks to perturb original data fields and
create synthetic training data. To demonstrate the effectiveness of the
proposed method, we conduct extensive experiments using real-world data from a
commercial conversational system. Based on the experiment results, the proposed
approach improves more than 80% (51 out of 63) of intents with less than 10K of
traffic instances in the skill-routing replication task.
