Evaluating Agent Interactions Through Episodic Knowledge Graphs (2022)

Abstract: We present a new method based on episodic Knowledge Graphs (eKGs) for evaluating (multimodal) conversational agents in open domains. This graph is generated by interpreting raw signals during conversation and is able to capture the accumulation of knowledge over time. We apply structural and semantic analysis of the resulting graphs and translate the properties into qualitative measures. We compare these measures with existing automatic and manual evaluation metrics commonly used for conversational agents. Our results show that our Knowledge-Graph-based evaluation provides more qualitative insights into interaction and the agent’s behavior.

## introduction in order to develop open-domain conversational agents, it is crucial to have automatic and reproducible ways of evaluating the interaction and the agent's role. however, interaction with people is challenging to evaluate for several reasons: 1) people behave differently in each interaction, 2) people appreciate the interaction for different reasons and aspects, 3) different goals and sub-goals may play a role simultaneously, and 4) personal relationships and past experiences have an impact on every interaction. for these reasons, most evaluations of interactive systems use human judges and questionnaires in analogy to user-satisfaction methods. in addition to these questionnaires, conversational systems are often evaluated by comparing system responses to human responses on a turnby-turn basis, where the prompts and the gold responses are taken from human-human conversations. standard measures such as blue @xcite , meteor @xcite , bertscore @xcite test the similarity between the system response and a gold response, whereas usr @xcite tests the coherence of the system response to the previous prompt and context. however, these measures do not truly assess the quality of the system's interpretation and the relevance of the prompt, and they punish systems for being creative and making responses personal. by detaching prompt-response pairs from the whole conversation, these metrics evaluate the reactivity of an agent, not its ability to engage in a coherent interaction. @xcite mention five general requirements for evaluation: 1) automatic to reduce human labour and subjectivity, 2) repeatable when applied to the same dialogue, 3) correlate with human judgments, 4) differentiated for various strategies, and 5) explainable. none of the existing approaches satisfy all these criteria. in this paper, we demonstrate that graph properties can be used as an additional and independent evaluation of the effectiveness of the communication. this evaluation is an automatic measure of semantic quality that is also explainable and reproducible, meeting three of the five previous requirements. we present a novel evaluation method that qualifies conversations using an episodic knowledge graph (ekg) @xcite . we calibrate different groups of graph measures in relation to human evaluations and ground truth independent measures. to test our selected metrics, we compare the quality of types of conversations and show that the proposed evaluation framework holds, regardless of differences in system design. our contributions are: ## related work dialogue systems have been studied for several decades and are further developed within conversational ai systems. in their survey, @xcite discuss different types of dialogue systems and how they are evaluated. they conclude that evaluating open conversational agents is an open problem due to the lack of a goal and variable structure. therefore, evaluation approaches focus on appropriateness and human likeness of responses or specific linguistic properties such as variability, lexical complexity, coherence, correctness, and relevance of system responses. evaluations can furthermore be done at a turn-level or conversationlevel. there is a many-to-many problem in both cases: multiple responses can be correct, multiple dialogues can lead to the right/same result, and every interaction is unique. attempts to automate these notions often rely on metrics such as blue @xcite , rouge @xcite , meteor @xcite , and bertscore @xcite to measure the similarity of the agent response to one or more ground-truth responses; or they borrow from information retrieval measures when systems need to select the appropriate responses from a set of possible alternatives. in contrast, usr @xcite is a new method created by fine-tuning roberta-base @xcite on the training set of topical-chat. whereas meteor and bertscore compare a system response to a ground-truth response, usr gives a quality evaluation score without a ground-truth response (reference-free) by measuring the coherence of the system response with the human prompt and the previous context. evaluation regimes with ground-truth responses limit agents' "freedom and creativity" to generate other responses that may also fit the purpose. therefore, it is unsurprising that evaluations often fall back on a posteriori evaluation by human judges. however, human evaluations suffer from several pitfalls: expensive, time-consuming, inconsistent across experiments, difficult to reproduce, and challenging to scale. researchers tried to harmonize the evaluation criteria to address the inconsistency and lack of coherence in terminology and methodology for human evaluations of open-domain dialogue. @xcite survey of 165 papers with human evaluations reports more than 200 quality criteria (such as fluency, accuracy, or readability.) that have been used in natural language generation. independently, @xcite analyzed the proceedings of the conference of intelligent virtual agents foot_0 between 2013 and 2018. they found 189 constructs from 89 questionnaires reported in 81 papers, which they reduced to 19 measurement instruments. measurements range from how enjoyable, correct and useful to how fluent. in an attempt to automate evaluations and make them more reproducible and scalable, the ninth dialog system technology challenge, track on interactive evaluation of dialog (dstc9, track 3) foot_1 carried out a variety of automatic and human evaluations on 33 systems submissions to the topical-chat challenge @xcite . topical-chat consists of conversations between two amazon mechanical turk workers who were given prior knowledge or information to refer to during their conversation. systems need to respond to turns from these conversations, replacing one worker. a human evaluation of system responses was done using the questionnaire from the fed dataset @xcite . an automatic evaluation was done using the measures meteor, bertscore, and usr. @xcite report that the usr (0.3 spearman) correlates better with human judgments than meteor (0.23 spearman) and bertscore (spearman 0.22), although they also admit that the correlation is not very high. it is still to be seen how easy usr can be transferred to other dialogues and contexts, as it was trained and tested on topical-chat. we present a reference-free approach that is not based on coherence but measures the interpretability of (multimodal) situations and accumulates these over time. the basic idea is that effective interactions result in rich and high-quality representations that can be measured in a knowledge graph. factors determining the communication effect are the agent's response quality and the collaboration between participants. ## problem formalization we represent an interaction as a series of tuples @xcite , such that: @xmath0, a set of time points @xmath1, a set of situations @xmath2, a set of graphs @xmath3, a set of unknown features part of situation s @xmath4, a set of defined properties that can make up graph g a graph g represents the interpretation of a sequence of situations s at time point t. each situation s can be modeled as a bundle of unknown features f and each graph g as a set of properties p that are defined a priori. to quality the conversations, we measure how many and which properties p are extracted from each turn and the cumulative effect of adding these properties over time to the graph g. the instrument's effectiveness in measuring quality depends on the ability to detect the properties p given the features f. the effectiveness of the communication depends on the predefined properties p that are chosen for the evaluation. the properties used to define the quality of the conversation can be 1) mathematical, e.g. measuring the average degree and sparseness, 2) semantic, e.g. number and type of triples, 3) knowledge integrity, e.g. conflicts, outliers, analogies, completeness, 4) subjective values, e.g. sentiment and emotion, certainty, trust, and 5) dialogue properties, e.g. turn-property ratio's, utterance type distribution and density, style and quality of expression. these measures depend on the capability to extract p from the unknown features f -implicit in image, audio and text signals-and the modelling of these properties in the knowledge graph g. an interaction will result in a series of graphs over time. a cumulative graph can be seen as an episodic knowledge graph (ekg) @xcite for which the qualitative evaluation over time can provide valuable additional insight. in this paper, we implement the above formal model and propose a set of properties p, defined as rdf triples, that correlate with human judgements and can be used across different conversational setups and for different property detection systems. ## capturing interaction and episodic knowledge in order to apply graph metrics, we need to generate properties from the (multimodal) signals produced during the interaction. as we represent these properties as rdf triples, we rely on a text-to-triple instrument to detect these properties. this instrument's effectiveness determines the evaluation's depth, precision and richness; therefore, the most precise and standardised instrument is preferable. however, using the same instrument across interactions, we can compare the interactions and draw conclusions about its conversational variables. another factor for our evaluation is the graph properties modelled in the ekg. some properties are generic and can be measured in any graph, whereas other properties depend on the semantics of the data model. in the following subsections, we discuss conversational variables when comparing interactions, the metrics that can be applied to the generated graphs and how they depend on specific property types. ## experimental setup human -machine conversations three groups of students had two conversations with leolani @xcite during which they had to introduce themselves. students were instructed to converse for 5 to 10 minutes or 30-40 turns per conversation. they were also instructed about the type of sentences and expressions from which the agent could extract triples to make the conversations more successful. conversations will be more cumbersome in real open settings where users do not know what the agent can understand. ## comparing within conversational variables to measure the quality of multiple interactions across different participants with the same agent, we collected human judgements of the humanmachine conversations and compared these with the graph metrics groups. ## comparing across conversational variables three major factors determine the resulting graph: 1) the (human) participant, 2) the agent's capability to understand the prompts, and 3) the agent's capability to respond adequately. our ekg-based evaluation, therefore, genuinely evaluates interaction from both ends. this makes it possible to evaluate interaction across different (types of) people with the same and/or different agents, resulting in different graphs due to the human input and/or the agents' capabilities. ## conclusion we presented a new method for evaluating dyadic interactions that does not require a ground-truth interaction or a human judgment a posteriori. our method analyses the episodic knowledge graph that results from interpreting prompts. like the usr score, our method is automatic and referencefree. however, we provided evidence that our evaluation correlates better with human judgments and gives deeper insight into the knowledge built up from the conversation. we also conclude that the graph metrics provide nuanced information about the growth of knowledge resulting from the interaction. although we cannot yet say anything about the absolute score of an interaction, we can compare different interactions based on the resulting graph and observe differences in graph properties that may or may not be desirable. note that this line of work aims to approximate human evaluations in a cost-free manner. yet, human evaluations are generally highly subjective and not reproducible to evaluate a conversation. thus, even though overall human ratings highly correlate to ekg sparseness, outliers arise due to the impact of individual judgments per student group. in future work, we want to validate these metrics by evaluating benchmark datasets, similar to the methodology by @xcite . we also want to direct the conversations to aim for certain types of knowledge and perspectives to validate that our metrics can detect such different intentions. we want to demonstrate that we can associate graph structures with functionality, e.g., having broad or deep knowledge of subjects, being able to direct people to trustworthy informants, or directing people to shared interests.