The schema-guided paradigm overcomes scalability issues inherent in building
task-oriented dialogue (TOD) agents with static ontologies. Instead of
operating on dialogue context alone, agents have access to hierarchical schemas
containing task-relevant natural language descriptions. Fine-tuned language
models excel at schema-guided dialogue state tracking (DST) but are sensitive
to the writing style of the schemas. We explore methods for improving the
robustness of DST models. We propose a framework for generating synthetic
schemas which uses tree-based ranking to jointly optimise lexical diversity and
semantic faithfulness. The generalisation of strong baselines is improved when
augmenting their training data with prompts generated by our framework, as
demonstrated by marked improvements in average joint goal accuracy (JGA) and
schema sensitivity (SS) on the SGD-X benchmark.

使用树形排序的生成模型，优化模型的语法多样性和语义忠实度，使其适用更广泛的任务。将合成的数据添加到基线模型的训练集中，可以显著提高其平均联合目标准确性和模式灵敏度。

基于树形重述排名的更健壮的模式引导对话状态跟踪

More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking

Developing mechanisms that flexibly adapt dialog systems to unseen tasks and
domains is a major challenge in dialog research. Neural models implicitly
memorize task-specific dialog policies from the training data. We posit that
this implicit memorization has precluded zero-shot transfer learning. To this
end, we leverage the schema-guided paradigm, wherein the task-specific dialog
policy is explicitly provided to the model. We introduce the Schema Attention
Model (SAM) and improved schema representations for the STAR corpus. SAM
obtains significant improvement in zero-shot settings, with a +22 F1 score
improvement over prior work. These results validate the feasibility of
zero-shot generalizability in dialog. Ablation experiments are also presented
to demonstrate the efficacy of SAM.

通过显式地提供特定任务对话策略，提出了 Schema Attention Model (SAM) 和改进版 Schema 表示来解决零 - shot 迁移学习问题，在 Star 语料库上验证了 SAM 在零 - shot 模式下的显著改善，F1 值提高了 22 个点。