Why Should I Turn Left? Towards Active Explainability for Spoken Dialogue Systems. (2021)

Abstract: In this paper we argue that to make dialogue systems able to actively explain their decisions they can make use of enthymematic reasoning. We motivate why this is an appropriate strategy and integrate it within our own proof-theoretic dialogue manager framework based on linear logic. In particular, this enables a dialogue system to provide reasonable answers to why-questions that query information previously given by the system.

## introduction thus far, explainability in dialogue systems has been implemented to the extent of passive explainability, where the developer of a dialogue system evaluates a system's decisions based on analysing the system's internal state (either ml-based or rulebased). systems themselves lack the capacity to explain their decisions and conclusions in the way humans can understand. we call such an ability active explainability. natural language dialogue is an efficient way of creating trust between users and systems, since it allows users to get answers to any doubts and questions they might have. "how do you know?" and "why?" questions are especially important, and are answered by explaining the reasoning behind a claim or suggestion. such explanations may be given at different levels of detail, and users may occasionally want more details than given by the system. providing an explanation which is optimally adapted to the individual user's level of expertise and general background knowledge is a very difficult task. this can be made considerably easier if the user is allowed to ask for additional information, challenge the system with counter-arguments, and establish a sense of common ground with the system regarding relevant background information. this means there needs to be an interaction between user and system. the system can build trust with the user by answering questions about the claims and decisions made. the paper is organised as follows. in section 2 we further motivate the importance of reasoning capabilities for conversational reasoning. section 3 discusses our approach to reasoning in natural human dialogue, and explores the notions of topos and enthymeme that can be employed in spoken dialogue systems. in section 4 we briefly present our proof-thoretic framework for dialogue management, which we further extend with a rudimentary support of active explainability (section 5). we conclude in section 6, discussing further directions of relevant research. ## background current artificial conversational agents are severely limited in their ability to reason. this reduces their usefulness to relatively simple tasks that do not involve much (or any) reasoning, which means that a huge number of tasks are out of reach for them. in the following genuine example interaction (queried 14th january 2020) the system fails to provide a simple inference from known facts: siri: president trump is 73 years old. user: how old is president macron? siri: president macron is 42 years old. user: is president trump older than president macron? siri: here is what i found on the web (displays irrelevant web links about brigitte macron) by contrast, humans engaged in real dialogue can make more complicated inferences such as example 2 below. lee conveys that he is well enough to play football but not well enough to go to school because football takes place outdoors. his father dave infers that if lee is well enough to play football then he is well enough to go to school. lee: cos of my cough. dave: how can you play football and not go to school then? lee: cos i was going out in the fresh air, i'm alright when i'm out in the fresh air. dave: so why aren't you going to school then? lee: i'm in the classroom all day dad. @xcite while the state of the art in conversational agents does not yet reflect that of computational models of inference, the reasoning capabilities displayed by dave and lee in example 2 are beyond the current reach of both fields. this study aims at advancing the current state of research towards enabling artificial conversational agents to achieve such reasoning capabilities. ## dialogue manager architecture we believe that it is crucial to use formal tools which are most appropriate for the task: one should be able to express the rules of various genres of dialogue in a concise way, free, to any possible extent, of irrelevant technical details. in the view of @xcite this is best done by representing the information-state of the agents as updatable sets of propositions. very often, dialogue-managument rules update subsets (propositions) of the information state independently from the rest. a suitable and flexible way to represent such updates are as function types in linear logic. the domain of the function is the subset of propositions to update, and the co-domain is the (new) set of propositions which it replaces. by using well-known techniques which correspond well with the intuition of information-state based dialogue management, we are able to provide a fully working prototype of the components of our framework: 3. several examples which use the above to construct potential applications of the system. ## discussion currently, if the system has access to multiple topoi, the choice of which topos the system should prioritise when making a suggestion is made at random. even if the suggestion is not the one preferred by the user, being given a reason based on an acceptable topos will enable the user to evaluate the suggestion and decide whether or not to go along with it. however, a useful extension would be to add a probabilistic component. for example, this would enable a system to learn that a particular user ranks certain topoi higher when making various decisions, thus adapting the topoi on which to base instructions according to that user's profile. one of the ways to achieve this would be to represent user profiles as vector of real values for each preference and have a vector real-valued information about the qualities of the item (e.g., characteristics of each route). then, we compute a score for each item as the sum of item's qualities, weighted by user's preferences for each quality and select the one with the best score. for the selection the topos can be recorded (in the simplest case as a fact like "the route is shorter which corresponds to your preference"). later, during the course of the dialogue, the topoi can be challenged and user preferences can be updated accordingly using bayesian updates. we leave the detailed description of this process for future work. in future investigations, it should be possible to assume that the publicised enthymemes and their corresponding topoi become a part of the information-state, allowing further discussion and, possibly, re-ranking of topoi. it should also be possible to learn topoi directly from the user, for instance, in the form of requests such as "if the caller is a vip, remind me to call back within 1 hour". in the future we plan to assess the trust placed in a system by users under different conditions, in order to find out if the reliability of a system that uses topoi is rated higher by users than one that does not (for instance, using subjective evaluation methods, like @xcite , and how much adapting the ranking of topoi to a specific user's profile contributes to building trust in the system on the part of the user.