Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation (2022)

Abstract: Implicit knowledge, such as common sense, is key to fluid human conversations. Current neural response generation (RG) models are trained to generate responses directly, omitting unstated implicit knowledge. In this paper, we present Think-Before-Speaking (TBS), a generative approach to first externalize implicit commonsense knowledge ( think ) and use this knowledge to generate responses ( speak ). We argue that externalizing implicit knowledge allows more efficient learning, produces more informative responses, and enables more explainable models. We analyze different choices to collect knowledge-aligned dialogues, represent implicit knowledge, and transition between knowledge and dialogues. Empirical results show TBS models outperform end-to-end and knowledge-augmented RG baselines on most automatic metrics and generate more informative, specific, and commonsense-following responses, as evaluated by human annotators. TBS also generates knowledge that makes sense and is relevant to the dialogue around 85% of the time

## introduction human communication strives to achieve common ground, consisting of mutual beliefs and common knowledge @xcite . such common ground depends not only on utterances, but also implicit knowledge. for example, in figure 1 , this common ground includes the relevant implicit background knowledge "rose is a type of flower". integrating such common ground in utterances is an implicit process often referred to as knowledge grounding @xcite . recent state-of-the-art neural response generation (rg) models based on pre-trained language models (lm) mostly produce responses in an endto-end manner @xcite figure 1: a motivating example for our study. we look to train models to externalize the implicit knowledge grounding step by explicitly generating knowledge before responding. 2020a; @xcite , i.e., models are trained to take history and produce a response. since implicit knowledge is unstated in dialogue history, rg models do not explicitly learn knowledge grounding and may generate uninformative and hallucinated responses @xcite @xcite . knowledge-grounded rg @xcite @xcite addresses this issue, however, most approaches require a knowledge base (kb) to retrieve knowledge for rg @xcite @xcite , which may suffer from the limited knowledge coverage of the used kbs. some work also casts knowledge as a latent factor in generation @xcite , which makes it hard to examine the quality of knowledge generation and how exactly rg uses the implicit knowledge, posing interpretability concerns. we propose think-before-speaking (tbs), an rg framework that trains the rg model to explicitly generate the implicit knowledge and use this knowledge to generate a response, inspired by inquiry-based discovery learning @xcite . we argue that this decomposition brings three major benefits: 1) compared with end-to-end rg, generated knowledge augments and/or constrains rg to produce more informative responses; 2) compared with knowledge-retrieval models, explicitly generating intermediate groundings can potentially generalize to knowledge not included in kbs and synergize with the rg process; 3) explicitly generated implicit knowledge used in rg provides a faithful explanation of the response intent. this new rg paradigm poses three main challenges: (1) how to identify implicit commonsense knowledge associated with dialogue turns for training the knowledge generation module; (2) how to represent structured knowledge in natural language (nl) for neural generative models; and (3) how to integrate knowledge and dialogues while distinguishing implicit and explicit parts in responses. to collect knowledge associated with each dialogue instance for training the tbs generative model, we propose weak supervision procedures to automatically align knowledge with each dialogue turn, rather than manually collecting humanannotations, which is expensive and unscalable. this is achieved by using conceptnet @xcite as our knowledge base and different matching approaches to identify the implicit knowledge. we explore several ways to format knowledge originally represented as structured triples into natural language so that rg models can adapt to the knowl-edge+response generation task easily. we experiment with structured triples, triples converted to natural language, and a more colloquial question answering format. to ensure a smooth transition between knowledge and dialogues, we consider using special symbols or prompts as separators. to evaluate the tbs framework, we introduce new evaluation protocols to cover different aspects of the system, including response quality, knowledge quality, and how tbs models leverage generated knowledge. we conduct extensive human evaluations for different variants of our training procedure. our experimental results show that our models produce more informative, specific, and responses that make more common sense compared to end-to-end rg models and other knowledgeaugmented models such as knowledge-selection. knowledge quality analysis shows that at least 85% of generated knowledge makes sense and is relevant, and the generated novel knowledge (not in conceptnet) also has high quality. furthermore, our tbs model even outperforms an rg model that takes in knowledge obtained using ground-truth responses, showing that explicitly generating implicit knowledge is a promising direction for response generation in open domain dialogue systems. ## problem formulation our tbs rg paradigm extends the traditional rg setting by incorporating an additional component of implicit knowledge in the generation process to externalize the knowledge grounding step in rg. ## learning to generate implicit knowledge by self-talk this section introduces our proposed tbs method to train a generative model that can both talk with itself to explicitly generate background commonsense knowledge (p θ (i|h ′ ) ) and then generate response afterwards, p θ (r|h ′ , i). figure 2 illustrates the process to train the tbs models. to pair each dialogue with appropriate implicit knowledge, we first define a matching process and use concept-net @xcite as the implicit knowledge source (section 3.1). then, to construct training instances, we face two key method design choices: how to represent knowledge (3.2) and how to connect the knowledge with the dialogue (3.3). finally, we train tbs rg models to learn p θ (i|h ′ ) and p θ (r|h ′ , i) with the same parameters θ. the following sections explain these components in details. ## results by evaluating our tbs model variants with other baselines, we aim to address the following questions: 1) do tbs models produce better responses than standard end-to-end rg models? 2) compared with other approaches to retrieve or generate additional knowledge, is tbs more helpful for rg? 3) do tbs rg models generate knowledge that makes sense and is relevant to the dialogue context? 4) do tbs models faithfully leverage the generated knowledge? ## conclusion inspired by how humans contribute to the common ground during communication, we propose to train rg models that explicitly generate implicit knowledge and then respond (tbs). this brings us three main benefits compared with prior end-toend rg models: 1) more informative and coherent responses by augmenting with knowledge; 2) generated knowledge provides faithful explanations of rg model's inner-workings; 3) models do not rely on external knowledge bases in response gen-eration time. we first identify implicit knowledge in dialogues, explore different knowledge representation and transition choices, and demonstrate promising results compared with end-to-end and knowledge-grounded rg models from extensive evaluations. we find strong and promising results for tbs rg model compared with end-to-end rg. in particular, tbs can produce good quality and novel knowledge, outperform end-to-end rg models despite training on less data, and even produce better responses than rg models that take groundtruth knowledge. we hope our findings encourage more future studies on making rg models better emulate human communication process and produce better-quality responses.