The collection and annotation of task-oriented conversational data is a costlyand time-consuming manner. Many augmentation techniques have been proposed to improve the performance of state-of-the-art (SOTA) systems in new domains that lack the necessary amount of data for training. However, these augmentation techniques (e.g. paraphrasing) also require some mediocre amount of data since they use learning-based approaches. This makes using SOTA systems in emerging low-resource domains infeasible. We, to tackle this problem, introduce a framework , that creates synthetic task-oriented dialogues in a fully automatic manner, which operates with input sizes of as small as a few dialogues . Our framework uses the simple idea that each turn-pair in a task-oriented dialogue has a certain function and exploits this idea to mix them creating new dialogues . We evaluate our framework within a low-resource setting by integrating it with a SOTA model TRADE in the dialogue state tracking task and observe significant improvements in the fine-tuning scenarios in several domains . We conclude that this end-to-end dialogue augmentation framework can be a crucial tool for natural language understanding performance in emerging task-oriented dialogue domains.
As the creation of task-oriented conversational data is costly, data augmentation techniques have been proposed to create synthetic data to improve model performance in new domains . Up to now, these learning-based techniques (e.g. paraphrasing) still require a moderate amount of data , making application to low-resource settings infeasible. To tackle this problem, we introduce an augmentation framework that creates synthetic task-oriented dialogues , operating with as few as 5 shots . Our framework utilizes belief state annotations to define dialogue functions of each turn pair. It then creates templates of pairs through de-lexicalization, where the dialogue function codifies the allowable incoming and outgoing links of each template. To generate new dialogues, our framework composes allowable adjacent templates in a bottom-up manner . We evaluate our framework using TRADE as the base DST model, observing significant improvements in the fine-tuning scenarios within a low-resource setting . We conclude that this end-to-end dialogue augmentation framework can be a practical tool for natural language understanding performance in emerging task-oriented dialogue domains.
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uses the simple idea that each turn-pair in a task-oriented dialogue has a certain function and exploits this idea to mix them creating new dialogues
utilizes belief state annotations to define dialogue functions of each turn pair. It then creates templates of pairs through de-lexicalization, where the dialogue function codifies the allowable incoming and outgoing links of each template. To generate new dialogues, our framework composes allowable adjacent templates in a bottom-up manner
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