Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents
Table 1: Data distribution and splits. ‘All’ contains the combined Movie and Entertainment live datasets
['domain', 'subset', 'carriers', 'signatures', 'slots', 'words']
While the data splits were stratified, signatures with fewer than four carriers were placed only in the train set, leading to the discrepancy in signature numbers across partitions. To examine the usefulness of the generated data for an extrinsic ask, we perform intent classification, a standard task in NLU. Our classifier is a BiLSTM model. Our setup follows two steps: First, the data generators are trained on ‘all’ train sets, and used to generate phrases for the dev sets (‘all’ and movies). Second, the intent classifier is trained on the ‘all’ train and dev sets (baseline), vs the combination of ‘all’ train, dev and generated synthetic data, which is our proposed approach. We evaluate on the ‘all’ and movies test sets, and use macro-averaged F-score across all intents as our metric.