Probing Commonsense Explanation in Dialogue Response Generation (2021)

Abstract: Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations. Aiming to close the gap between current response generation (RG) models and human communication abilities, we want to understand why RG models respond as they do by probing RG model’s understanding of commonsense reasoning that elicits proper responses. We formalize the problem by framing commonsense as a latent variable in the RG task and using explanations for responses as textual form of commonsense. We collect 6k annotated explanations justifying responses from four dialogue datasets and ask humans to verify them and propose two probing settings to evaluate RG models’ CSR capabilities. Probing results show that models fail to capture the logical relations between commonsense explanations and responses and fine-tuning on in-domain data and increasing model sizes do not lead to understanding of CSR for RG. We hope our study motivates more research in making RG models emulate the human reasoning process in pursuit of smooth human-AI communication.

## introduction response generation (rg) systems, which have the basic goal of mimicking human conversation, have as of yet an unmeasured ability to understand communicative intents. in general, standard neural language models build correlative models of linguistic stimuli rather than deep understanding of humanlevel meaning @xcite . as such, there is reason to suspect that, while rg systems today have impressive performance on common metrics @xcite , they achieve this performance without truly understanding human communication. commonsense reasoning (csr), defined as "the basic level of practical knowledge and reasoning concerning everyday 1 our code and data are on our project page: https:// sites.google.com/usc.edu/cedar . situations and events that are commonly shared among most people" @xcite , is critical in human communication. specifically, csr helps establish a common ground consisting of "mutual knowledge" between participants, which is key to smooth communication @xcite . for example, consider a conversation between two friends shown in figure 1 . the reason the person on the right (responder) is happy is not indicated explicitly, but it is common sense that finding a buyer for the house (that the responder is likely aiming to sell) makes one happy, which explains the response "i'm so happy". motivated by how humans communicate, we ask a main research question: do rg models understand the implicit csr that explains why a response makes sense? this will help us analyze whether the rg models that seem to produce human-like responses really understand the reasoning process that justifies the response, which is important to build a reliable and robust dialogue system. furthermore, understanding implicit common sense behind rg can also help make models generate more natural and coherent responses.to answer this important research question, we present our initial findings from annotating commonsense explanations in dialogues and evaluating rg models for commonsense reasoning capabilities. we first present a probing setup for evaluating common sense in rg, called cedar: common sense in dialogue response generation. we start with formalizing csr in rg by considering common sense as a latent variable that helps explain the observed variable "response" in the rg process -similar to how humans use common sense in communication @xcite . to instantiate implicit common sense for probing, we use textual explanations of the response as the common sense embedded in the dialogue context. to understand whether rg models can comprehend implicit common sense, we corrupt explanations to break the logical coherence and compare model behaviors between a valid explanation and a corrupted one. to operationalize the probing, we collect the first annotations on commonsense explanations that justify dialogue responses. each annotation is a dialogue-specific explanation that explicitly describes what might cause the response in one of the five dimensions: event, emotion, location, possession, and attribute, inspired by human cognitive psychology @xcite . we find through pilot studies that directly asking people to annotate result in explanations with high variation and subjectivity, to account for this, we first generate candidate explanations by adopting a large textto-text language model trained on a story explanation dataset, namely glucose @xcite , under the dialogue setting. next, we conduct a carefully designed two-stage human verification process with a qualification test and the main annotation task. we present our findings from verifying 6k generated explanations on 1,200 dialogues sampled from four public dialogue datasets. using the annotated explanations, we probe state-of-the-art (sota) rg models for two csrrelated abilities: (i) the ability to understand whether the commonsense explanation can justify a response, and (ii) the ability to attribute logically-coherent explanations for dialogue responses. these are inspired by what showcases human understanding of common sense in conver-sational communication. our probing setup contrasts valid explanations with corrupted version. corruptions are generated via two methods: logical corruptions that disrupt logical coherence, and complete corruption where we disrupt the grammatical naturalness of the sentence. we find that the models fail to understand common sense that elicits proper responses according to performance on our probing settings and some models even do not distinguish gibberish sentences. fine-tuning on in-domain dialogues and verified explanations do not help with understanding. we also find interesting cases that show potential statistical biases in rg models. we hope our annotated explanations and probing findings encourage more studies on making rg models communicates with deep understanding of human reasoning process. ## task formulation and challenges this section first introduces how we incorporate common sense as a latent variable in the rg setting. then we specify two challenges that arise in order to examine whether rg models can comprehend common sense to arrive at responses similarly as humans do. lastly, we present our solutions to the challenges by instantiating common sense as textual explanation and proposing two probing settings to evaluate if models reason about common sense when generating responses. ## generating commonsense explanations for dialogue responses to get explanation annotations for dialogue responses, we first automatically generate commonsense causal explanations and then manually verify via crowdsourcing. we use a text-to-text model trained on commonsense story explanation dataset glucose @xcite as the generator and conduct 2-stage human verification on generated explanations. we first introduce the model we use, the adaptation of the model on dialogue data, and our verification process to ensure the quality of generated explanations. ## probing setup we probe rg models' capability of understanding and using the explanation e in a contrastive manner (sec. 2.2). this section first introduces the corruption types under two categories, then we introduce evaluation metrics, and finally we discuss several sota rg models with different neural architectures that we probe. ## probing results and analysis we present results and findings for our two probing settings using different dialogue rg models across four datasets for which we collected verified explanations. for each human-validated explanation, we generate a corrupted version using one of our six corruption types, and compare the nll for the probe target according to our two settings. in tables 1 and 2 , we show both binary accuracy and average difference in nll for dialogues from four datasets under the two settings and aggregate the six corruption types into two categories. we also sample 5% of the dialogues for human verification under the same two probe settings. ## conclusion we study commonsense reasoning in dialogue response generation aiming to close the gap between current rg models and human communication abilities. specifically we formalize the problem by framing commonsense as a latent variable in the rg task and using explanations for responses as textual form of commonsense. we design an explanation collection procedure for rg and propose two probing settings to evaluate rg models' csr capabilities. we hope our study motivates more research in making rg models emulate human reasoning process in pursuit of smooth human-ai communication.