VERVE : Template-based R eflecti VE Rewriting for M oti V ational I nt E rviewing (2023)

Abstract: Reflective listening is a fundamental skill that counselors must acquire to achieve proficiency in motivational interviewing (MI). It involves responding in a manner that acknowledges and explores the meaning of what the client has expressed in the conversation. In this work, we introduce the task of counseling response rewriting, which transforms non-reflective statements into reflective responses. We introduce VERVE, a template-based rewriting system with paraphrase-augmented training and adaptive template updating. VERVE first creates a template by identifying and filtering out tokens that are not relevant to reflections and constructs a reflective response using the template. Paraphrase-augmented training allows the model to learn less-strict fillings of masked spans, and adaptive template updating helps discover effective templates for rewriting without significantly removing the original content. Using both automatic and human evaluations, we compare our method against text rewriting baselines and show that our framework is effective in turning non-reflective statements into more reflective responses while achieving a good content preservation-reflection style trade-off.

## introduction during the covid-19 pandemic, the number of people living with anxiety and depression rose more than four times, thus aggravating the ongoing disparity between unmet needs for mental health treatment and increased mental health disorders @xcite . one driving cause behind this discrepancy is the shortage of mental health professionals, which is exacerbated by the fact that becoming a counselor requires extensive training @xcite . in particular, counselor training is difficult to speed up due to several factors, such as the need for expert supervision, and the laborious and time-extensive process needed to provide evaluative feedback. there figure 1: in this example of counselor response rewriting, a counseling trainee is asked to provide a reflective response given the client prompt and produces a poor response by giving a piece of advice rather than reflecting the client's concerns. our system generates an improved response that preserves content and increases the use of reflective language. however, the progress in developing tools that can fulfill a "mentoring role" and offer alternative language suggestions for counselors in training has been limited. to fill this gap, we introduce the task of counselor response rewriting, which involves rephrasing trainees' responses with basic counseling skills into alternative responses that reflect a more advanced level of counseling proficiency. we focus on reflective listening as our main counseling skill, and on motivational interviewing @xcite as the counseling strategy. we show an example of our system output in figure 1 . in this case, providing a numerical score or a reference reflection (i.e., a high-quality reflection) does not help the counselor understand what parts of their answer could be improved. our system addresses this shortcoming by separating the behavior-relevant (e.g., reflection-like language) and the behavior-non-relevant parts, and using the latter as a template for creating an improved rewrite of the original. we introduce verve (reflective rewriting for motivational interviewing), a framework based on template editing methods from text style transfer that do not require parallel data, since expert annotation of rewritten responses is expensive and time-consuming. we propose two simple techniques to adapt template-based text rewriting to the counseling domain: paraphrase-augmented training, and adaptively template updating. the first helps the text generator to learn a more flexible mapping between a masked template and a full response so that the structure of the final rewrite is not constrained by the template. the second handles the content-edit trade-off (e.g., preserving part of the user response rather than completely rewriting) by iteratively updating the masked template based on the effect of the rewrite. we evaluate our framework against several baselines from previous text style transfer works using automatic evaluation and demonstrate that our system outperforms baselines in achieved reflection scores while still preserving content from the original response. ## related work our work builds upon previous work in text style transfer, text rewriting, and nlp for counseling. broadly, counselor response rewriting is related to text rewriting in nlp, which includes, text style transfer, content debiasing, and controlled generation @xcite . in this work, we focus on rewriting through templatebased editing (or prototype-based in other text style transfer literature @xcite . these systems offer several advantages over alternative frameworks such as latent style transfer or llm-based methods @xcite . first, template-based editing systems offer high interpretability as they rely on predefined templates or patterns. users can have precise control over the editing process by selecting specific templates or designing new ones. this allows for easier understanding and manipulation of the output, which is particularly important in applications where trans-parency is valued. moreover, content preservation is another advantage of prototype-based editing, since the template generation process can be controlled to vary the amount of original content preserved in the rewrite. an important difference from previous studies is that we address text rewriting in dialog context, whereas previous studies are mostly concerned with transforming isolated text, such as product reviews @xcite . since counseling reflections often include empathy @xcite , empathetic text generation and rewriting are also relevant. while most of the empathetic generation literature focuses on modeling emotion for generating responses from scratch, @xcite directly models multiple aspects of empathy and applies reinforcement learning (rl)-based training for rewriting online mental health comments. similarly, we leverage a classifier model for discriminating attribute labels for text but use simple supervised learning instead of policy gradient rl training. our work is also related to recent work on nlp for the counseling domain aiming to assist counselors during their practice and ongoing training. reflection is an important construct in counseling strategies such as mi, and previous works have studied how the frequency or quality of reflections can be used to evaluate counseling @xcite @xcite . there also have been studies on generating reflections @xcite . however, to the best of our knowledge, our work is the first to consider rewriting non-reflections into reflections, . ## datasets we use two publicly available mi datasets from pair @xcite and annomi @xcite . while pair is a collection of single turn exchanges, annomi is a set of counseling conversations consisting of multiple conversational turns. pair contains client prompts along with counselor responses with varying reflection quality levels, including simple and complex reflections or non-reflection. complex reflections go further than simple reflections (i.e., simple repetition or slight rephrasing of client's statement) by inferring unstated feelings and concerns of the client and are often preferred over simple reflections in mi counseling @xcite . note that although pair contains multiple responses for a given prompt they were not designed as rewrites, and thus they cannot be used directly as parallel data to train a supervised end-to-end rewriter. preprocessing. we preprocess annomi to focus on single exchanges between counselors and clients. also, since annomi does not include annotations for reflection type we use a the subset of utterances labeled as no-reflections only. we extract pairs consisting of a single client turn followed by a counselor non-reflection, with constraints on the length of the utterances to filter out short utterances or disfluencies. we include a more detailed descrip-tion of the datasets and the filtering procedure we used in appendix a. the final dataset statistics are shown in ## methodology our verve framework, shown in figure 2 , is based on a template editing-based approach that does not require parallel data. below, we describe system details. ## experiments during our experiments, we use a 75%/5%/20% split of the pair data for train, development, and test sets, and use annomi for evaluation only. we report the average scores for five runs based on different random seeds. ## discussion does template editing work for counselor response rewriting? we argue that template editing is a useful strategy for rewriting counselor responses. however, several adaptations are needed to apply it to the counseling domain. for instance, the prompt should be considered as an additional input and the mask template should be modified accordingly. we found that attention-based token masking is a better fit for response rewriting than n-gram-based masking in @xcite ; @xcite since the relationship between prompts and responses can be naturally modeled by the former. moreover, it is helpful to model a flexible mapping between templates and reconstructions, because response rewriting may require a greater amount of text editing than style transfer. when should rewrites be suggested? measuring content preservation and the usefulness of text rewriting are still open problems. however, in the context of counselor response rewriting in mi, we can provide a few guidelines based on our empirical findings. first, we should consider the quality of the original counselor response. ## conclusion in recent years, the disparity between accessible and timely mental health care and the increasing demand for psychotherapy has significantly deteriorated, highlighting the need for more scalable and efficient ways to train new counselors. nlp can assist this counselor training process through automated feedback, which previously was only available through expert supervision. in this paper, we introduced the task of counselor response rewriting to generate automatic counseling feedback. we introduced verve, a templatebased approach with paraphrase-augmented training and adaptive template updating, which can transform non-reflective counselor responses into reflective responses. without access to parallel data, verve achieves a higher editing effect than other baseline systems by using flexible template reconstruction approaches. it also has the ability to adjust the attribute masking step without unnecessarily sacrificing content preservation. in future work, we plan to pilot our system in educational settings and explore how verve can provide support for student training or coaching. the verve system is publicly available from https://github.com/mindojune/verve .