The Candide model: How narratives emerge where observations meet beliefs (2023)

Abstract: This paper presents the Candide model as a computational architecture for modelling human-like, narrative-based language understanding. The model starts from the idea that narratives emerge through the process of interpreting novel linguistic observations, such as utterances, paragraphs and texts, with respect to previously acquired knowledge and beliefs. Narratives are personal, as they are rooted in past experiences, and constitute perspectives on the world that might motivate different interpretations of the same observations. Concretely, the Candide model operationalises this idea by dynamically modelling the belief systems and background knowledge of individual agents, updating these as new linguistic observations come in, and exposing them to a logic reasoning engine that reveals the possible sources of divergent interpretations. Apart from introducing the foundational ideas, we also present a proof-of-concept implementation that demonstrates the approach through a number of illustrative examples.

## introduction today's natural language processing (nlp) systems excel at exploiting the statistical properties of huge amounts of textual data to tackle a wide variety of nlp subtasks. they meticulously capture the co-occurrence of characters, words and sentences, sometimes in relation to an annotation layer, and make use of numerical operations over these cooccurrences to perform mappings between linguistic input on the one hand and task-specific linguistic or non-linguistic output on the other. as a result of recent advances in neural machine learning techniques and infrastructure (see e.g. @xcite @xcite , combined with the availability of huge text corpora, impressive results are now being achieved on many tasks, including machine translation, speech recognition, text summarisation, semantic role la-belling and sentiment analysis (for an overview, see @xcite . yet, current nlp systems are everything but capable of modelling human-like, narrative-based language understanding. one reason is that this capacity is hard to cast in the predominant machine learning paradigm. indeed, human-like narrative understanding is hard to define in the form of an annotation scheme. narratives are not captured in texts as such, but are construed through an interpretation process. this process is personal, and different individuals may construe different narratives given the same linguistic observations @xcite . this diversity in perspectives reflects the richness of human language and cognition, and modelling divergent interpretations constitutes a crucial challenge to the broader computational linguistics community today. the primary objective of this paper is to introduce a novel approach to narrative-based language understanding that starts from the idea that narratives emerge through the process of interpreting novel observations with respect to previously acquired knowledge and beliefs. concretely, we present a computational model of this interpretation process. the model integrates three main components: (i) a personal dynamic memory that holds a frame-based representation of the knowledge and beliefs of an individual agent, (ii) a construction grammar that maps between linguistic observations and a frame-based representation of their meaning, and (iii) a reasoning engine that performs logic inference over the information stored in the personal dynamic memory. crucially, the representations that result from the language comprehension step take the same form as those stored in the personal dynamic memory. not only does this mean that these representations can dynamically be merged into the personal dynamic memory to update the knowledge and beliefs of an agent, it also facilitates the use of information stored in the personal dynamic memory to inform the language comprehension process. the information stored in the personal dynamic memory can be queried through a logic reasoning engine, with each answer being supported by a human-interpretable chain of reasoning operations. this chain of reasoning operations explains how the background knowledge and beliefs of an agent guide its conclusions, thereby revealing the narrative construed through the agent's interpretation process. personal, dynamic and interpretable models of narrative-based language understanding are of great interest to the fields of computational linguistics and artificial intelligence alike. to the field of computational linguistics, they contribute a perspective that emphasises the individual and contextualised nature of linguistic communication, which contrasts with the static and perspective-agnostic models that dominate the field of nlp today. in the field of artificial intelligence, they respond to the growing interest in the development of artificial agents that combine human-like language understanding with interpretable, value-aware and ethicsguided reasoning (see e.g. @xcite @xcite . the remainder of this paper is structured as follows. section 2 lays out the background and overall architecture of our model. section 3 presents its technical operationalisation and provides a number of illustrative examples. finally, section 4 reflects on the contribution of our paper and discusses avenues for further research. ## the candide model the model for narrative-based language understanding that we introduce in this paper is named after voltaire's "candide ou l'optimisme" (voltaire, 1759). it is inspired by one of the main themes of the novel, namely that a character's belief system and history of past experiences shape the way in which they interpret the world in which they live. as such, different characters in the novel represent different philosophical positions and thereby construe different narratives to explain the same situations and events. the main protagonist, candide, starts out as a young, naive 'blank slate'. through conversations with the leibnizian optimist pangloss and the fatalistic pessimist martin, and as a result of long travels that make him experience the hardships of the world, candide gradually develops his own belief system in light of which he ever more wisely interprets the situations and events he witnesses. following the main theme of the novel, our aim is not to model a single 'true' interpretation of an observation, but to show that different beliefs can lead to different interpretations. moreover, we consider the belief system of an agent to be dynamic, with the interpretations and conclusions of an agent shifting as more experience and knowledge are gathered. in order to formalise these highlevel ideas, we introduce the following operational definitions: personal dynamic memory (pdm) the personal dynamic memory of an agent is a data structure that stores the knowledge and beliefs of the agent in a logic representation that supports automated reasoning. the pdm is conceived of as a dynamic entity to which new knowledge and beliefs can be added at any point in time. reasoning over the pdm is non-monotonic, as updated beliefs can alter conclusions. belief system the belief system of an agent at a given point in time equals all information that is stored in the agent's pdm at that moment in time. each entry in the pdm carries a confidence score, which reflects the degree of certainty of the agent with respect to that entry. however, there exists no formal or conceptual distinction between entries based on their epistemological status, avoiding the need to distinguish between 'knowledge', 'facts', 'opinions' and 'beliefs' for example. conclusion a conclusion is a piece of information that logically follows from a reasoning operation over the belief system of an agent. a typical example would be the answer to a question. narrative a narrative is defined as a chain of reasoning operations that justifies a conclusion based on the belief system of an agent as it is stored in its pdm. logically, it corresponds to a proof for the conclusion. it is possible that multiple narratives that support the same or different conclusions can be construed by an individual agent. an agent can use the certainty scores carried by the beliefs that constitute its pdm to rank its conclusions and the narratives that support them. language comprehension language comprehension is the process of mapping a linguistic observation, such as an utterance, paragraph or text, to a logic representation of its meaning. while language comprehension is primarily concerned with retrieving the information captured in the linguistic input, rather than its integration with respect to the personal dynamic memory, it is heavily intertwined with other aspects of the interpretation process as well. indeed, the linguistic knowledge needed to support language comprehension is personal and dynamic, and thereby unavoidably constitutes a first layer of individual interpretation. ## technical operationalisation this section presents the technical operationalisation of an initial proof-of-concept of the candide model. we discuss the proof-of-concept's language comprehension component, its personal dynamic memory, and its processes of reasoning and narrative construction. ## discussion and conclusion in this paper, we have introduced the candide model as a computational architecture for modelling human-like, narrative-based language understanding. as such, we have presented an approach that radically breaks with today's mainstream natural language processing paradigm. rather than modelling the co-occurrence of characters and words in huge amounts of textual data, our approach focusses on the logic reasoning processes that may justify different interpretations of the same linguistic observations. while this forces us to take an enormous leap back, it bears the promise of contributing a perspective that emphasises the individual and contextualised nature of linguistic communication to the fields of computational linguistics and artificial intelligence. we have defined narratives to be chains of reasoning operations that underlie the conclusions drawn by an individual based on their belief system. this belief system is personal and dynamic in nature, as it is continuously being shaped by new linguistic and non-linguistic experiences. narratives are thus not captured in texts as such, but need to be construed through a personal interpretation process. a narrative thereby reflects the perspective of an individual on the world, as the process of narrative construction necessarily takes one's entire belief system into account. the construction of a narrative is a means rather than an end. while the end is to reach a conclusion, for example to answer a question, to resolve a coreference, or to make sense of a novel observation or experience, the means to reach that end is to construe a narrative that is consistent with one's belief system. in this view, the construction of a narrative is not a task in itself, but serves the purpose of solving an external task through humaninterpretable reasoning processes. as narratives highly depend on external tasks and individual belief systems, they are hard to annotate in linguistic resources. indeed, whether a narrative is justified or not only depends on whether it is consistent with the input that is observed in combination with the beliefs held by an individual. narrative-based language understanding therefore largely coincides with the use of explainable methods for solving a variety of nlp tasks, including question answering, text summarisation and sentiment analysis, with the difference that the focus in evaluation shifts from the task accuracy to the soundness of the reasoning processes involved. the candide model operationalises this vision through a combination of frame-based constructional language processing and logic reasoning. as such, the belief system of an agent is represented as a collection of facts and rules that support automated reasoning through logic inference. the fluid construction grammar-based language comprehension component is used to map between natural language utterances and a frame-based representation of their meaning. this semantic representation makes use of the same format as the one used to represent the agent's belief system, facilitating the straightforward integration of new beliefs into the agent's personal dynamic memory. the prolog-based reasoning component can be leveraged to solve external tasks by proving logic formulae based on the facts and rules stored in the agent's personal dynamic memory. it is during this process of logic inference that narratives emerge as logical explanations that justify the conclusions drawn by an agent. we have illustrated our proofof-concept implementation of the candide model by means of a didactic example that shows how two agents who hold slightly different beliefs interpret the same linguistic observation differently, as they construe different narratives that lead to substantially different conclusions. while this paper has laid the conceptual foundations of a novel approach to narrative-based language understanding, it has left the issue of operationalising the approach on a larger scale unaddressed. we envision an agent to start out as a blank slate, with an empty belief system and grammar. through experience, an agent would then gradually build up linguistic and non-linguistic beliefs in a constructivist manner through the processes of intention reading and pattern finding. these processes have abundantly been attested in children (see e.g. @xcite and have more recently been operationalised at scale in artificial agents through abductive reasoning processes (see e.g. @xcite @xcite . we consider these preliminary results to be modest yet promising steps towards the moonshot of building personal, dynamic and human-interpretable models of narrative-based language understanding.