We investigate the role of prompt-based demonstrators in improving natural language generation for coaching-oriented dialogue systems in different languages. These systems present significant challenges due to their need for semantically accurate, goal-driven responses across diverse dialogue act taxonomies and languages. We define three types of prompt demonstrators, i.e., pairs of meaning representation-utterance, that include different degrees of specification in such meaning representation. We then fine-tune pretrained language models separately for four very different languages and evaluate how the specificity of these demonstrators affects the quality of the generated sentences. Our experiments show that more specific prompts lead to more coherent and accurate outputs, particularly for low-resource languages and small models. Additionally, we observe promising zero-shot performance with larger models, showing a complementary value of prompts. These results demonstrate that simple prompting strategies, combined with fine-tuning, can significantly improve output quality in complex dialogue generation tasks across languages.

Open domain spoken dialogue systems need to controllably generate many different dialogue acts (DAs) to allow Natural Language Generation (NLG) to create interesting and engaging conversational interactions with users. We aim to create an NLG engine that can produce a variety of DAs that make substantive knowledge-grounded contributions to a conversation. Training such an NLG typically requires dialogue corpora that are labelled for DAs, which are expensive to produce and vulnerable to quality issues. Here, we present a prompt-based learning approach to transfer DAs from one domain, video games, to 7 new domains. For each novel domain, we first crawl WikiData to create Meaning Representations that systematically vary both the number of attributes and hops on the WikiData Knowledge Graph. The proposed method involves a self-training step to create prompt examples for each domain followed by an overgeneration and ranking step. The result is a novel, high-quality dataset, Wiki-Dialogue, of 71K knowledge-grounded utterances, covering 9 DAs and the Art, Movies, Music, Sports, TV, Animal, and Boardgames domains, whose combined DA and semantic accuracy is 89%. We assess the corpus quality using both automatic and human evaluations and find it high. The corpus is found to be safe, lexically rich, and large in vocabulary, when compared to similar datasets.