Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays (2023)

Abstract: One important aspect of language is how speakers generate utterances and texts to convey their intended meanings. In this paper, we bring various aspects of the Construction Grammar (CxG) and the Systemic Functional Grammar (SFG) theories in a deep learning computational framework to model empathic language. Our corpus consists of 440 essays written by premed students as narrated simulated patient–doctor interactions. We start with baseline classifiers (state-of-the-art recurrent neural networks and transformer models). Then, we enrich these models with a set of linguistic constructions proving the importance of this novel approach to the task of empathy classification for this dataset. Our results indicate the potential of such constructions to contribute to the overall empathy profile of first-person narrative essays.

## introduction much of our everyday experience is shaped and defined by actions and events, thoughts and perceptions which can be accounted for in different ways in the system of language. the grammatical choices we make when writing an essay (i.e., pronoun use, active or passive verb phrases, sentence construction) differ from those we use to email someone, or those we utter in a keynote speech. "word choice and sentence structure are an expression of the way we attend to the words of others, the way we position ourselves in relation to others" @xcite . such choices allow us to compare not only the various options available in the grammar, but also what is expressed in discourse with what is suppressed @xcite . given the great variability in the modes of expression of languages, the search for an adequate design of grammar has long motivated research in linguistic theory. one such approach is cxg @xcite @xcite which prioritizes the role of constructions, conventional form-meaning pairs, in the continuum between lexis and syntax @xcite . as such, these constructions form a structured inventory of speakers' knowledge of the conventions of their language @xcite . another particular grammatical facility for capturing experience in language is halliday's system of transitivity as part of the systemic functional grammar (sfg) @xcite , a theory of language centred around the notion of language function. sfg pays great attention to how speakers generate utterances and texts to convey their intended meanings. this can make our writing effective, but also give the audience a sense of our own personality. however, unlike cxg, halliday's system of transitivity describes the way in which the world of our experience is divided by grammar into a 'manageable set of process types' @xcite each offering not only a form-meaning mapping, but also a range of stylistic options for the construal of any given experience through language. in stylistics, researchers have used this model to uncover and study the grammatical patterns through which texts can enact a particular ideology, or an individual's distinctive 'mind style' of language @xcite . the idea of 'style as choice' in halliday's transitivity system can be best understood as experiential strategies (like avoiding material processes or repeating passive voice constructions) such as those identified as contributing to a reduced sense of awareness, intentionality or control in the human agent responsible @xcite . such an individual is often said to appear 'helpless @xcite , or 'disembodied' @xcite . take for instance, construction choices like 'i reassured her' vs. 'she was reassured', or "i greeted her upon entrance" vs. "the nurse greeted her upon entrance" vs. "she was greeted upon entrance" -which show the degree of agency and intended involvement on the part of the agent in the action. such linguistic choices often occur together in stylistic profiling exercises to showcase the techniques contributing to 'passivity', or the degree of suppression of agency and power in characterisation @xcite . in this paper, we try to bring cxg and sfg closer together in the study of discourse level construction of arguments for the analysis of empathic content of narrative essays. specifically, inspired by research in critical discourse analysis, we are taking a step further to show ways in which such construction choices can manipulate (and even reduce) the attention we give to the agency and moral responsibility of individuals @xcite . specifically, such form-meaning-style mappings can be used to capture the point of view as an aspect of narrative organization and the perspective through which a story is told, the way the characters are portrayed in terms of their understanding of the processes they are involved in, as well as their own participation in the story. in this respect, "narratives seem necessary for empathy [..] they give us access to contexts that are broader than our own contexts and that allow us to understand a broad variety of situations" @xcite . they provide a form/structure that allows us to frame an understanding of others, together with a learned set of skills and practical knowledge that shapes our understanding of what we and others are experiencing. drawing on halliday's transitivity framework rooted in systemic functional linguistics, this paper attempts to reveal the (dis)engaged style of empathic student essays from a semantic-grammatical point of view. specifically, we want to investigate how certain types of processes (i.e., verbs) and constructions (i.e., passive voice) function to cast the essay writers (as main protagonists and agents) as perhaps rather ineffectual, passive, and detached observers of the events around them and of the patient's emotional states. we take a narrative approach to empathy and explore the experiences of premed students at a large university by analysing their self-reflective writing portfolios consisting of a corpus of firstperson essays written by them as narrated simulated patient-doctor interactions. the corpus has been previously annotated and organized @xcite following established practices and theoretical conceptualizations in psychology @xcite @xcite . computationally, we introduce a set of informative baseline experiments using state-of-the-art recurrent neural networks and transformer models for classifying the various forms of empathy. as initial experiments show relatively low scores, we measure the presence of several grammatical structures, leveraging halliday's theory of transitivity, and its correlation with the essays' overall empathy scores. we apply this framework to state-of-the-art and representative neural network models and show significant improvement in the empathy classification task for this dataset. although previous research suggests that narrative-based interventions tend to be effective education-based methods, it is less clear what are some of the linguistic mechanisms through which narratives achieve such an effect, especially applied to empathy, which is another contribution of this research. ## related work in spite of its increasing theoretical and practical interest, empathy research in computational linguistics has been relatively sparse and limited to empathy recognition, empathetic response generation, or empathic language analysis in counselling sessions. investigations of empathy as it relates to clinical practice have received even less attention given the inherent data and privacy concerns. most of the research on empathy detection has focused on spoken conversations or interactions, some in online platforms (e.g. @xcite @xcite @xcite , very little on narrative genre @xcite , and even less in clinical settings. @xcite used crowdsourced workers to self-report their empathy and distress levels and to write empathic reactions to news stories. @xcite built a text corpus of student peer reviews collected from a german business innovation class annotated for cognitive and affective empathy levels. using batson's empathic concern-personal distress scale @xcite , @xcite have focused only on negative empathy instances (i.e., pain and sadness "by witnessing another person's suffering"). however, empathy is not always negative @xcite . a dataset reflecting empahatic language should ideally allow for expressions of empathy that encompass a variety of emotions, and even distinguish between sympathy and empathy. 1 following a multimodal approach to empathy prediction, r. m. @xcite and @xcite identify sequential patterns of empathy in video-recorded exchanges between medical graduates and cancer patients. @xcite analyzed the discourse of conversations in online peer-to-peer support platforms. novice writers were trained to improve low-empathy responses and provided writers with adequate feedback on how to recognize and interpret others' feelings or experiences. in follow-up research, they performed a set of experiments @xcite whose results seemed to indicate that empathic written discourse should be coherent, specific to the conversation at hand, and lexically diverse. to our knowledge, no previous research has investigated the contribution of grammatical constructions like halliday's transitivity system to the task of empathy detection in any genre, let alone in clinical education. 2 3 self-reflective narrative essays in ## constructions and stylistic profiles in empathic narrative essays inspired by the success of halliday's transitivity system on cognitive effects of linguistic constructions in literary texts @xcite , we also examine a set of construction choices which seem to co-occur in texts as material and mental actions or events. in our quest of understanding empathy expression in student narrative essays, we want to test if such contributions lead to a reduced sense of intentionality, awareness or control for the agentive individual represented (i.e., the essay writer in the role of the doctor), and thus, identifying the stylistic profile of the narrative. specifically, these constructions are: human actor + process (ha+p); body part + process (bp+p); other inanimate actor + process (ia+p); goal + process (g+p) (see drawing from @xcite , nuttall (2019) also notes that these experiences can vary in forcedynamic (energetic) quality and thus sentences exhibiting an energetic tone are linked with 'high' transitivity and those with lower or static energy can be linked to 'low' transitivity. in order to identify energetic sentences, we leverage the ibm watson tone analyzer api @xcite which assesses the emotions, social propensities, and language styles of a sentence. we denote sentences containing high extroversion and high confidence (@xmath0.8) as energetic. sentences with low scores are marked as static. 61.77% of the sentences exhibit a static tone, energetic tone being less frequent. in sfg, active and passive voice plays an important role as well. @xcite shows that, in some genres, text indicating a lower degree of agentive control tends to use more passive voice constructions. as this is also relevant to our task, we test whether voice contributes indeed to a reduced sense of intentionality, awareness or control for the agent (in particular the essay writer playing the doctor's role) and how these features correlate with the overall empathy score at essay level. using an in-house grammatical-role extraction tool developed on top of spacy's dependency parser, we find that 66% of sentences use active voice and 34% passive voice. foot_6 77.92% of active-voice sentences exhibit human actor subjects and only 22.08% include non-human actors. similarly for passive voice, the majority (83.09%) of sentences had human actors. comparing frequencies of active and passive voice across various essay empathy score ranges (figure 1 ), we notice that higher empathy essays (@xmath1. stylistic research has also shown (nuttall, 2019) the importance of movement of body parts as nonhuman agents. we, too, parsed sentences for the use of body parts, i.e. eyes, arms, head and curated a list based on anatomical terminology as defined by wiktionary.org (2022) resulting in about 18.61% of the dataset sentences (statistics for top 5 most common bodyparts are in ## empathy classification task our ultimate goal is to build an informed and performant classifier able to determine the degree of empathetic content of a medical essay overall and at sentence level. taking advantage of form-meaningstyle mappings in the language system, in this paper, we built and test a number of state-of-theart classifiers enriched with varied constructions and stylistic features ( ## conclusions medical education incorporates guided selfreflective practices that show how important it is for students to develop an awareness of the emotional and relational aspects of the clinical encounter with their patients @xcite . the way people identify themselves and perform in particular roles and in relation to others brings together a specific set of values, attitudes, and competencies that can be supported through ongoing self-reflection. such interactions can be captured in language via constructions as part of cxg and halliday's transitivity system. in this paper, we bring various aspects of these theories in a deep learning computational framework to model empathetic language in a corpus of essays written by premed students as narrated simulated patient-doctor interactions. we start with baseline classifiers (state-of-the-art recurrent neural networks and transformer models). then, we enrich these models with a set of linguistic constructions proving the importance of this novel approach to the task of empathy classification for this dataset. our results indicate the potential of such constructions to contribute to the overall empathy profile of first-person narrative essays. figure 4 shows two examples of essays, one with low empathy and one with high empathy, highlighted with the themes: medical procedural information (cyan), empathetic language (yellow), and both (green). neither sentences are not highlighted. it is interesting to see that in essay (a), the sentences mentioning diet and exercise were not identified as medical procedural information given that they were not found in dr. kavita ganesan's work on clinical concepts @xcite .