Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking
This work addresses the challenge of controllable natural language generation for dialogue systems, offering an incremental improvement over existing prompt-based methods by incorporating automatic ranking for both dialogue act and attribute accuracy.
The paper tackles the problem of generating dialogue responses with specific dialogue acts and high semantic accuracy by introducing a few-shot overgenerate-and-rank approach using LLMs, achieving near-perfect semantic accuracy (99.81%) and outperforming few-shot fine-tuning methods.
Dialogue systems need to produce responses that realize multiple types of dialogue acts (DAs) with high semantic fidelity. In the past, natural language generators (NLGs) for dialogue were trained on large parallel corpora that map from a domain-specific DA and its semantic attributes to an output utterance. Recent work shows that pretrained language models (LLMs) offer new possibilities for controllable NLG using prompt-based learning. Here we develop a novel few-shot overgenerate-and-rank approach that achieves the controlled generation of DAs. We compare eight few-shot prompt styles that include a novel method of generating from textual pseudo-references using a textual style transfer approach. We develop six automatic ranking functions that identify outputs with both the correct DA and high semantic accuracy at generation time. We test our approach on three domains and four LLMs. To our knowledge, this is the first work on NLG for dialogue that automatically ranks outputs using both DA and attribute accuracy. For completeness, we compare our results to fine-tuned few-shot models trained with 5 to 100 instances per DA. Our results show that several prompt settings achieve perfect DA accuracy, and near perfect semantic accuracy (99.81%) and perform better than few-shot fine-tuning.