Traditional systems designed for task oriented dialog utilize knowledge
present only in structured knowledge sources to generate responses. However,
relevant information required to generate responses may also reside in
unstructured sources, such as documents. Recent state of the art models such as
HyKnow and SeKnow aimed at overcoming these challenges make limiting
assumptions about the knowledge sources. For instance, these systems assume
that certain types of information, such as a phone number, is always present in
a structured knowledge base (KB) while information about aspects such as
entrance ticket prices, would always be available in documents.
In this paper, we create a modified version of the MutliWOZ-based dataset
prepared by SeKnow to demonstrate how current methods have significant
degradation in performance when strict assumptions about the source of
information are removed. Then, in line with recent work exploiting pre-trained
language models, we fine-tune a BART based model using prompts for the tasks of
querying knowledge sources, as well as, for response generation, without making
assumptions about the information present in each knowledge source. Through a
series of experiments, we demonstrate that our model is robust to perturbations
to knowledge modality (source of information), and that it can fuse information
from structured as well as unstructured knowledge to generate responses.

该研究使用 MutliWOZ-based 数据集，演示了当前方法在去除信息源严格假设时表现显著下降，并且利用预先训练的语言模型 fine-tune 了一个基于 BART 的模型，可以融合结构化和非结构化知识生成响应。