Reason first, then respond: Modular Generation for Knowledge-infused Dialogue (2022)

Abstract: Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. While retrieval-augmented models help alleviate this issue, they still face a difficult challenge of both reasoning to provide correct knowledge and generating conversation simultaneously. In this work, we propose a modular model, Knowledge to Response (K2R), for incorporating knowledge into conversational agents, which breaks down this problem into two easier steps. K2R first generates a knowledge sequence, given a dialogue context, as an intermediate step. After this “reasoning step”, the model then attends to its own generated knowledge sequence, as well as the dialogue context, to produce a final response. In detailed experiments, we find that such a model hallucinates less in knowledge-grounded dialogue tasks, and has advantages in terms of interpretability and modularity. In particular, it can be used to fuse QA and dialogue systems together to enable dialogue agents to give knowledgeable answers, or QA models to give conversational responses in a zero-shot setting.

## introduction to be regarded as successful, a conversational agent needs to generate utterances that are both knowledgeable and factually correct, as well as being conversationally appropriate, fluent and engaging. the pursuit of this goal has led to ever bigger models that store a large amount of knowledge in their parameters @xcite @xcite . however, hallucination -wherein a model generates factually inaccurate statements -has remained a problem no matter the size of the model @xcite . recent advances in neural retrieval models have made some inroads into this problem @xcite @xcite on both the dialogue context and by learning to retrieve documents containing relevant knowledge. however, the conversational setting is challenging because these models are required to perform multiple duties all in one shot: to perform reasoning over the returned documents and dialogue history, find the relevant knowledge, and then finally combine this into a conversational form pertinent to the dialogue. perhaps due to this complexity, it has been observed that failure cases include incorporating parts of multiple documents into one factually incorrect response, or failure to include knowledge at all and reverting instead to a generic response using the dialogue context only. in this work, we instead propose to decompose this difficult problem into two easier steps. specifically, by first generating pertinent intermediate knowledge explicitly and then, conditioned on this prediction, generating the dialogue response. we call this model knowledge to response (k2r). using this modular design, we can train and evaluate the reasoning performance of the model independently from its conversational abilities, increasing the interpretability of our model's output. this also allows us to plug external knowledge into dialogue systems without any requirement for retraining, for example, from question answering systems. the dialogue response model's task reduces to incorporating the predicted knowledge in an engaging and context-fitting conversational response. we conduct extensive experiments across multiple tasks and datasets. we find that our k2r model effectively improves correct knowledge-utilization and decreases hallucination @xcite in knowledge-grounded dialogue @xcite . in open-domain dialogue, the k2r model improves the performance on automatic metrics compared to its seq2seq counterpart, along with the additional benefits of increased interpretability of the model's output and the possibility for knowledge injections. the modular design allows us to fuse state-of-the-art pre-trained qa modelswithout any fine-tuning -with dialogue models to generate answers that humans judge as both more knowledgeable and engaging. our modular system also outperforms multi-tasking approaches. our code and generated dataset is made publicly available foot_0 . ## related work improving dialogue systems by increasing their knowledgeability has been tried in several different ways: from integrating knowledge bases @xcite @xcite , to larger models that are pre-trained on more data @xcite @xcite , and recent neural retrieval models @xcite . knowledgegrounded open-domain dialogue datasets @xcite @xcite foster the research and development of knowledge-aware generative dialogue models. a known issue of such models, referred to as "hallucination", is that they mix up facts and generate factually inaccurate statements. @xcite try to alleviate hallucination by using recent advancements in retrieval-augmented generative models developed for opendomain qa tasks @xcite . these methods still hallucinate to some degree, and their predictions (and hence errors) are not easily interpretable. there is also recent work in the space of modular or intermediate generation components for text generation. the approach of text modular networks promises more interpretable answers to multi-hop questions @xcite @xcite . @xcite learn a generative model that decomposes the task in the language of existing qa models for hotpotqa @xcite and drop @xcite . @xcite solve text-to-sql tasks with intermediate text representations. for storytelling, hierarchical generation procedures have been proposed @xcite . in reinforcement learning settings, generating natural language has been used as an intermediate planning step @xcite , and in particular in goaloriented dialogue @xcite and open-domain qa @xcite as well. for summarization tasks, the work of @xcite proposes an intermediate autoencoder latent representation. similarly, @xcite apply the information bottleneck principle to find an intermediate compressed sentence that can best predict the next sentence. for knowledge-grounded dialogue, an approach using internet search can also be seen as a modular intermediate step, where the search query is first generated @xcite . in that sense retrieval-based qa has also been seen as a modular technique in many studies @xcite . previous work has also explored the intersection of qa and dialogue models from multiple different angles. the dream dataset @xcite consists of multiple-choice questions about a conversation. @xcite propose a questionanswering task based on dialogue histories of the tv show friends. the quac @xcite and coqa @xcite datasets are designed to have the questions asked in the conversational flow, with possibly, multiple follow-ups. however, while these datasets require a model to understand a dialogue's history, the target responses are short-form answers. therefore, these tasks do not train a dialogue model that generates an engaging, conversationally appropriate response; instead, they result in a qa model that understands dialogue-structured context. ## k2r model we propose a two-step model for generating dialogue responses called knowledge to response (k2r). instead of directly mapping from dialogue history (context) to response, it generates an intermediate sequence output which is the knowledge basis for the next utterance. conceptually, our k2r model consists of two parts: the two models can potentially share parameters (or even be the same model), and the two steps would then be differentiated by context tokens in the input. alternatively, the two models can be completely separate and trained on different resources, allowing plug-and-play modularity. we explore both these options in this work. ## conclusion in this work, we presented k2r: a modular approach for knowledge-based dialogue models. we showed that by decomposing the knowledge step and response generation into explicit sequence-tosequence subtasks, we could improve dialogue systems by incorporating knowledge or turning short qa model answers into an appropriate conversational form. in detailed experiments, we showed that this modular system helps with hallucination in knowledge-grounded dialogue, is rated by humans as more knowledgeable and engaging when answering questions, and improves generation metrics on open-domain dialogue. furthermore, it allows for more interpretable results and supports knowledge injection. future work should continue to investigate methods with modular reasoning steps to help in difficult language tasks.