Measure only what is measurable: towards conversation requirements for evaluating task-oriented dialogue systems (2025)

Abstract: Chatbots for customer service have been widely studied in many different fields, ranging from Natural Language Processing (NLP) to Communication Science. These fields have developed different evaluation practices to assess chatbot performance (e.g., fluency, task success) and to measure the impact of chatbot usage on the user’s perception of the organisation controlling the chatbot (e.g., brand attitude) as well as their willingness to enter a business transaction or to continue to use the chatbot in the future (i.e., purchase intention, reuse intention). While NLP researchers have developed many automatic measures of success, other fields mainly use questionnaires to compare different chatbots. This paper explores the extent to which we can bridge the gap between the two, and proposes a research agenda to further explore this question.

## introduction we need to talk about measurement requirements. there is a vast body of literature on the evaluation of dialogue systems, with a wide range of different methods to assess different properties of the conversations that people have with chatbots and the impressions that are formed during those conversations. the goal of many natural language processing (nlp) researchers seems to be to avoid asking people about their experiences because human evaluation is costly and time-consuming. however, most of the literature rests on the assumption that the properties that we are interested in are measurable from the conversational data. we question this assumption and ask: to what extent do chatbot conversations contain useful cues to determine conversation quality (and beyond)? although this question is relevant for all kinds of chatbots, we will focus on task-oriented dialogue systems. as we will argue, nlp researchers mostly focus on intrinsic properties of these systems ( §2) while organisations are often more interested in extrinsic evaluation ( §3 ). an open challenge in dialogue research is to predict the users' opinion of the system based only on their conversation. to tackle this challenge, we believe it is essential to think about the requirements for us to be able to say more about what users think about chatbots. for this, we need to study conversational richness ( §4,5). based on an overview of the existing literature, we propose a roadmap for future research ( §6). where possible, nlp researchers tend to prefer automatic metrics to quantify system performance, since automatic approaches are generally cheaper and faster @xcite . depending on the purpose of their study, nlp researchers may even choose not to let their system interact with people at all, but rather to have the system engage in simulated (parts of) conversations (e.g., @xcite . this allows them to compare how different systems respond to the same utterances. for example, researchers may investigate how appropriate the different responses are (e.g., @xcite . when nlp researchers engage in human evaluation studies, they often do this through relatively superficial crowd-sourcing tasks, providing participants with responses in particular conversations, and asking questions about the quality of these individual responses (see e.g. @xcite , who ask human annotators to select the better system answer to a specific prompt). 1 additionally, nlp researchers have explored methods to automatically predict human judgements @xcite @xcite ). an interesting observation about this line of work is that nobody discusses the feasibility of the task; it is more or less assumed to be possible to predict the ratings, and the studies themselves show to what extent the authors' approach seems to work. finally, nlp research typically does not specify the properties of conversations for which the proposed approach should work. 2 ## the communication science perspective task-oriented chatbots are designed to be used, often by organisations aiming to alleviate the workload of their customer support agents. communication scientists may study the use of chatbots in such an organisational context. 3 the communication perspective differs from that of nlp researchers. the review of @xcite shows that nlp typically does not assess either the attitude of the customer support agent or the user's attitude towards the brand. while nlp thus mostly seem to focus on the quality of the interaction, researchers in business communication are more interested in the impact of chatbot interactions on users' experience and their 1 also note that there is typically only one question item per construct, leading to mono-operation bias. ## media richness and chatbots media richness refers to the idea that our means of communication differ in the kind and number of cues that they can process @xcite . for example, a text message does not carry any auditory information, whereas a telephone call does. videoconferencing introduces visual information but may still lack other features of face-to-face conversations: haptic cues such as touch and smell, but also the affordance to interact with the real world and manipulate objects together. researchers studying media richness may, for example, look at how the richness of different means of communi- ## conversation requirements when installing a piece of software on a computer, the computer needs to meet a particular set of system requirements for the software to run properly. we do not yet have any equivalent to system requirements (perhaps we could call these 'conversation requirements') for evaluation metrics. that leads us to ask: when is a conversation rich enough? different constructs have different requirements that need to be fulfilled before they can be operationalized through behavioural data. as we said before: lowlevel constructs such as the fluency of the system responses can already be measured in many cases. through fully written conversations we may also be able to determine the smoothness of the conversation, and given the full conversation, we are also able to determine task success. but what about the user's mood or their level of satisfaction? what kind and amount of information do we need to establish these? and what else can we learn from conversations with chatbots? correlates of extrinsic constructs. we do not have to start from scratch. researchers from different areas have found correlations between (non-)verbal behaviour and different mental states. for example, there is a long history in psychology of inferring writers' mental characteristics based on the words they use @xcite . researchers in the field of affective computing have worked to extract emotions from speech and facial expressions (for surveys, see: george and muhamed ilyas 2024; ballesteros et al. 2024). aside from emotions, other researchers have worked on audiovisual cues that signal uncertainty (e.g. @xcite , which may be a good indicator for when users are confused about the actions of the chatbot. similarly, previous work has compared textual cues of engagement to self-reported engagement, demonstrating that utterance level cues can predict engagement in chatbot conversations (he et al., 2024). 6 this leads us to: observation 4 there may be hope: a. proxies or antecedents of extrinsic constructs can be measured from interaction data. b. different researchers are working to identify relations between relevant variables (e.g. language use and level of confusion) of course, this kind of research is not without its drawbacks. tausczik and pennebaker (2010) are the first to admit that the relation between texts and authors' mental states is very complex, and existing text analysis methods are still relatively crude. furthermore, @xcite note that the relation between facial expressions and experienced foot_3 emotions may not be universal, and so it may be hard to draw reliable conclusions based on visual features alone. 8 finally, we again emphasize that the question is not just about whether one can in principle make the connection between what someone says and how they feel. we should also look at how much text, video, or audio is needed in order to make any inference at all, and how much data is needed for that process to be reliable. ## discussion in summary, we propose that research on evaluation metrics should pay more attention to the requirements for those metrics to work properly. these requirements should be operationalised using different richness dimensions, along the lines of ## conclusion this paper has discussed the evaluation of chatbots from two perspectives, nlp and communication science. communication scientists tend to focus more on constructs that nlp researchers would consider extrinsic: a shift in brand attitude may be a consequence of an interaction with a chatbot, whereas intrinsic constructs such as fluency are assumed to be measurable on the basis of interactions with the chatbot. we may be able to predict a user's brand attitude if the conversation is rich enough to contain clues about the user's stance towards the organisation that the chatbot represents. but when we take a step back, we have to acknowledge that this also holds for the measurement of fluency and so many other intrinsic constructs that nlp researchers seem to take for granted. when can we meaningfully assess any property? different constructs will have different conversation requirements, but we always have to take conversational richness into account.