Shared knowledge in natural conversations: can entropy metrics shed light on information transfers? (2022)

Abstract: The mechanisms underlying human communication have been under investigation for decades, but the answer to how understanding between locutors emerges remains incomplete. Interaction theories suggest the development of a structural alignment between the speakers, allowing for the construction of a shared knowledge base (common ground). In this paper, we propose to apply metrics derived from information theory to quantify the amount of information exchanged between participants, the dynamics of information exchanges, to provide an objective way to measure the common ground instantiation. We focus on a corpus of free conversations augmented with prosodic segmentation and an expert annotation of thematic episodes. We show that during free conversations, the amount of information remains globally constant at the scale of the conversation, but varies depending on the thematic structuring, underlining the role of the speaker introducing the theme. We propose an original methodology applied to uncontrolled material.

## introduction theories of interaction explain how participants elaborate their discourse in the perspective of exchanging information, executing a task, establishing a joint action, etc. these theories stipulate in particular that such activity is correlated with the building of a shared knowledge between participant, also called common ground @xcite . in these frameworks, the quality of an interaction depends on the capacity of building such mutual knowledge, which to its turn depends on the alignment of linguistic representations between participants. these mechanisms are based on different levels of convergence between the participants, that can occur at any level: lexical, syntactic, prosodic, as well as gestures, behaviors, etc. one hypothesis is that this phenomenon is also visible at the semantic level, showing a coordina-tion between participants in terms of information exchange that can be uncovered by studying the amount of such information and its dynamics during a conversation. the goal of this work is therefore to evaluate these questions by means of informationtheoretical measures @xcite : sharing information relies on the use of simple symbols which can be combined, concatenated to transfer increasingly complex knowledge. moreover, it is possible to analyze the dynamics of this process, whether the amount of transfer vary during a conversation, at what position, and whether an alignment between participants can also be observed at this level. an estimation of the quantity of information exchanged between participants and its dynamics could therefore constitute an objective way to measure the common ground instantiation. several works have been done in this direction, based on lexical information measured by entropy, and showing a convergence between participants. inspired by @xcite , we study the dynamics of information transfer at three levels: first globally, at the scale of an entire conversation, by taking into account productions from both speakers into a same system. doing that, we propose to identify whether some specific phenomena (e.g. peaks) appear in the amount of exchanged information and that could be related with discourse-level structures (e.g. topic shift). second, we will study the global evolution of entropy for each speaker, trying to exhibit some convergence patterns (e.g. phase synchronization). third, we propose to apply the same type of analysis at the scale of a topic, by studying the dynamics of information exchange within a topic (e.g. decrease of entropy) as well as the complementary patterns between speakers. last, but not least, this is the first work in this domain applied to unrestricted natural conversations. this paper presents several contributions, corresponding to important differences with the litera-ture. first, we propose to explore this question applied to free conversations instead of task-oriented or controlled ones. second, in difference with existing works, we evaluate the dynamics of the exchange based on well-defined inter-pausal units instead of sentences (a not adequate notion for spoken languages). finally, we base our analysis on thematic annotation made by an expert (human linguist) instead of an automatic topic segmentation. the paper is organised as follows. in section 2, we review the different approaches to these questions in the litterature. in section 3, we describe our conversational dataset and the methodology we apply. our experiments and a discussion of the results are presented in section 4. ## related works several studies have proposed to use informationtheoretic measures to study language processing. the general idea is to approach an evaluation of the cognitive load through quantitative estimation. in a seminal work, @xcite introduced the notion of surprisal, defined as the negative log-probability of a word given the preceding context, to measure processing difficulty. this approach has been picked up by many studies in psycholinguistic, showing in particular a correlation between reading times and surprisal @xcite . in the same vein, based on grammatical probability distributions, entropy reduction has been proposed to evaluate the informational contributions of each word as a complexity processing measure @xcite . at the lexical level, without any additional syntactic information than what is understood by the linguistic model, entropy has been proposed to estimate sentence information content in discourse. we offer in this section an overview of the main works done in this direction by first presenting the main approaches to measure information content and second the methods for studying variations of such measures at the discourse level. ## experiments in this section we present the results of our experiments with the paco-cheese dataset. taking the values of h(s) (i.e., the information content of a sentence) and h(s|c) (i.e., the contextualised entropy) estimated by the language model, we also compute the difference between contextualised and decontextualised entropy (mi). we extend results obtained by previous works with this new corpora containing free conversations. we then explore those results using qualitative and quantitative analysis of locations with high information content. ## conclusion the results presented in this paper represent a new contribution for the study of information exchange during conversation. first, this work only relies on free natural conversations, without adding more controlled corpora. in particular, in difference with other works in the literature, we do not add any task-oriented dialogue (such as maptask) nor telephone conversation (such as switchboard), that have known specific impacts on turn taking and topic shift. in terms of methodology, we decided to use a prosodic segmentation of the input (pauses longer than 200ms) generating identify inter-pausal units usually used in studies on spoken language. ipus are discourse segments with a certain coherence only identified on the basis of the acoustic signal. these segments offer a finer-grained view of the input in comparison with the segmentation into sentences that are usually used in the literature. this notion of sentence is not only problematic when applied to spoken language (the existing works do not precisely explain to what they correspond), but may also introduce a bias when studying topic shift, these two segments being possibly the same. finally, we are using with this analysis a thematic segmentation that was done manually by experts, rather than relying on automatic segmentation as previous works might have done. texttiling identifies topics based on semantic similarity; here annotations are based on higher-level information, bringing together all different linguistic and nonverbal information, providing a much more reliable segmentation. our results first confirm that at the scale of a conversation, entropy remains stable, as it has been observed in other works. at a local level, when segmenting the discourse in themes, we also observe a specific behavior, showing a decrease in the entropy of the speaker introducing the theme, which is expected. however, no significant pattern can be observed for the responder, for who the entropy remains approximately stable. to be more precise, we did not observe any increase in the entropy. as a consequence, we cannot say that a convergence in the entropy rate between the different speakers can be observed at the scale of a theme. this result is important in the study of conversational interactions. it means that convergence between speakers, which is necessary during a conversation, is a complex phenomenon that cannot be observed only on the basis of quantity measures. at the same time, the analysis of entropy constitutes a robust cue for evaluating how much and when information is transferred between speakers in a natural setup; however it must be complemented with data from other sources to assist the model in isolating truly important sections of the dialogue, from noise (rarer words that are logical in the context). this work opens the door to further study. for starters, as previously mentioned, enriching the models with information, coming from other modalities would most likely refine the analysis. among the modalities of interest are audio (speech rate is known to be modulated according to the difficulty of the information), video (gaze), and cerebral activity. indeed, we think that the dynamics of the entropy is correlated with information exchange and more generally with the building of the common ground. it becomes therefore possible to start studying the brain basis of mutual understanding by looking specifically at the brain signal associated with entropy peaks. our hypothesis is that this entropy-based indicator could offer the possibility to analyze the brain signal in a time-locked event paradigm (evoked-related potentials) as well as the