Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features (2021)

Abstract: Knowledge-grounded dialogue systems are intended to convey information that is based on evidence provided in a given source text. We discuss the challenges of training a generative neural dialogue model for such systems that is controlled to stay faithful to the evidence. Existing datasets contain a mix of conversational responses that are faithful to selected evidence as well as more subjective or chit-chat style responses. We propose different evaluation measures to disentangle these different styles of responses by quantifying the informativeness and objectivity. At training time, additional inputs based on these evaluation measures are given to the dialogue model. At generation time, these additional inputs act as stylistic controls that encourage the model to generate responses that are faithful to the provided evidence. We also investigate the usage of additional controls at decoding time using resampling techniques. In addition to automatic metrics, we perform a human evaluation study where raters judge the output of these controlled generation models to be generally more objective and faithful to the evidence compared to baseline dialogue systems.

## introduction dialogue systems that strive to be informative teachers are difficult to build, despite recent progress in training end-to-end systems that mimic human language at a linguistic level. these systems benefit from vast training data and great representational capacity; yet there are no controls (or training objectives) available that ensure they are truthful. a more limited goal for a system is to be faithful to one or more source documents that we implicitly trust. such a system might help educate users about a particular topic through conversational interaction, or it might augment a task-oriented dialogue system by providing additional information about i visit animal shelters fairly often a "no-kill" shelter is an animal shelter that does not kill healthy or treatable animals even when the shelter is full, reserving euthanasia for terminally ill animals ... ## task we introduce a sub-task of knowledge-grounded dialogue where a dialogue agent is intended to be informative and must not share hallucinations, which we define here as any information that is neither inferrable from nor directly stated by external documents. in this task, a system is given evidence from a document (or documents) and a conversation history and must produce a response that is both faithful to the evidence and also natural within the context of the previous conversation utterances. because this task focuses on being informative to a user, the agent is not allowed to share unsupported or subjective information (this includes invented personal traits -e.g. "i love dogs, too!"). additionally, it is not sufficient to be purely extractive as information from the evidence may need to be re-phrased to be a conversationally appropriate response (e.g. if a user asked a question that is inferrable from the evidence but not directly stated). to simplify the task for this paper, we assume that an appropriate evidence span, e, has already been labelled. we therefore study how to generate an appropriate response y given the previous conversation history x and a chosen evidence e as input. ## modeling we investigate how to add controllable features to a large neural dialogue model in order to constrain : in our modeling framework, a large pre-trained language model is used to encode the evidence and conversation history and produce a response. we incorporate additional tokens (i.e. control codes) to train the model to recognize differences between types of utterance that are more or less grounded to the evidence. at decoding time, we also investigate the significance of using resampling methods. the amount of hallucinated text while also taking advantage of the underlying fluency of a large endto-end neural model. ## experiments we perform experiments using automatic metrics and human judgments to evaluate the effectiveness of the proposed controllable dialogue system and its various components. ## conclusion in this paper, we investigate how to design knowledge grounded dialogue systems that are less prone to including hallucinations or subjective information. we discuss three evaluation measures related to the groundedness of the response and discuss two methods for integrating these metrics into a controllable dialogue system. we demonstrate that this controllable dialogue system is able to produce responses that are perceived by humans to be more objective and faithful to document-based evidence.