Knowledge Enhanced Reflection Generation for Counseling Dialogues (2022)

Abstract: In this paper, we study the effect of commonsense and domain knowledge while generating responses in counseling conversations using retrieval and generative methods for knowledge integration. We propose a pipeline that collects domain knowledge through web mining, and show that retrieval from both domain-specific and commonsense knowledge bases improves the quality of generated responses. We also present a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. We show that both retrieved and COMET-generated knowledge improve the system’s performance as measured by automatic metrics and also by human evaluation. Lastly, we present a comparative study on the types of knowledge encoded by our system showing that causal and intentional relationships benefit the generation task more than other types of commonsense relations.

## introduction mental health care has been of great importance as the ongoing covid-19 pandemic poses a serious negative impact on people's mental wellbeing @xcite . not only there is a larger unmet need for counseling services, the health care workers are also in tremendous physical and mental strain @xcite . with this in mind, it is natural to consider how the advancement in natural language processing can be leveraged to help counseling. across different counseling styles, reflective listening has always been a fundamental procedure underlying effective counseling practices @xcite . reflective listening asks the counselor not only to listen to the client carefully, but also to actively make a guess of what the client means. if carried out the right way, it gives the client a sense of being understood and facilitates further self-exploration. however, people do not always say what they mean, which is especially the case for patients seeking mental support. reflection, as the response made based on reflective listening, sometimes needs to decode the client's meaning not explicitly expressed in words. on the other hand, pressing the client to clarify the missing part may hinder them from expressing their own experience @xcite . thus, counseling frequently calls for counselors to make inferences based on their prior knowledge. for example, when the client says i had a really hard time sticking to my diet this week, a plausible reflection may be you're wondering whether you'll be able to lose weight this way, which relates diet with losing weight as an inference based on commonsense knowledge. moreover, making a good reflection may sometime require domain knowledge. for example, to understand the client in figure 1 , the counselor needs to know that smoking can be a possible cause of emphysema, and chantix is a medication for smoke cessation. all these cases pose challenges to state-of-the-art language models. in this paper, we propose the task of knowledge enhanced counseling reflection generation, which utilizes the dialogue context as well as commonsense and domain knowledge. this extra knowledge is needed since existing pre-trained language models struggle to produce coherent and informative responses that capture relevant knowledge, even if they have acquired some knowledge during the pre-training phase @xcite . a system that generates accurate counseling reflections can serve as a tool to aid counseling training or assist counselors during a session by providing alternative reflections in response to client's statements. we experiment with two main strategies to incorporate knowledge. the first is retrieval, which acquires sentences containing relevant knowledge based on the vector representations of sentences from the dialogue and assertions in the knowledge base using a bert-based model @xcite . the second strategy is generative, where we first extract key phrases from the dialogue, and query a comet model for plausible knowledge triplets with a predefined set of relations @xcite . we propose a knowledge-grounded bart @xcite model using soft positional encoding and masked self-attention representations to indicate the knowledge position and make the introduced knowledge only visible to the key phrase it relates to. in addition, we explore the effect of different knowledge sources on the counseling responses generation task. although commonsense knowledge bases usually have high coverage for general domain concepts, they contain a limited amount of domain-specific knowledge. this applies particularly to medical terminology. for instance, when querying conceptnet @xcite , a wellknown knowledge base, for the word chantix (a prescription smoking cessation aid) we are only able to retrieve three relationships, including synonyms, related terms, and type-of, whereas with a common word daughter conceptnet provides a total of eleven relationships. for the chantix example in figure 1 , conceptnet is also missing important causal relationships regarding side effects or suggested usage, which are especially relevant during a counseling conversation about smoking cessation. to address this challenge, we collect a dataset of counseling domain knowledge using web mining with queries constructed with the medical concepts extracted from the dialogue as well as manually defined templates. we compare this web-collected data with a public commonsense knowledge base, and show that this data collected with no human an-notation can serve as a complementary knowledge resource. we also conduct an ablation study on different categories of commonsense knowledge, and show that intentional or causal relationships are more useful for counseling response generation, a finding consistent with related medical literature. @xcite . contributions. the main contributions of this work are as follows: 1) we collect a counseling knowledge base and use it along with commonsense knowledge bases for the task of reflection generation using different retrieval-based methods. 2) we adopt the encoding scheme from k-bert on bart to incorporate knowledge generated from comet. 3) we analyze different types of commonsense and domain knowledge, and their effect on the generation task. 2 related work previous research has addressed the task of automating response generation in health care and counseling settings. greer et al. (2019) used a decision tree to deliver pre-written scripts and guide the user to learn a set of positive emotion skills. v et al. (2019) identified medical entities and the client's intent to fetch an answer for cancer related questions. almusharraf et al. (2020) classified client's responses to choose which question to ask next for smoking cessation. there are also commercial systems like woebot (fitzpatrick et al., 2017) that detect mental health issues mentioned by the user and direct them to relevant information. however, there is a limited amount of work on free-form generation as compared to the template-based approaches described above. shen et al. (2020) focused on generating counseling reflections with gpt-2 based on the dialogue context and responses retrieved from similar counseling sessions. we address a similar task but enhance the generation process by infusing commonsense and domain specific knowledge to better emulate what counselors do in practice. to the best of our knowledge, the effect of knowledge in counseling response generation is not yet well studied. large-scale pretrained language models have been shown to encode some knowledge implicitly through their pretraining objectives @xcite , including both commonsense @xcite and factual knowledge @xcite . however, pretrained language models still struggle with some downstream applications, especially when the model needs to make inference based on context @xcite . thus, recent works have also explored enhancing pretrained models with external knowledge. introducing knowledge into language models has been shown to be successful on various downstream tasks and model architecture @xcite @xcite . for instance, mao et al. (2019) generates story with multitasking learning on commonsense qa datasets. zhao et al. (2020) used bert as a knowledge selection module for dialogue generation. chakrabarty et al. (2020) ranked knowledge generated from the comet for sarcasm generation. ji et al. (2020) do multi-hop with a graph convolutional network on conceptnet. similarly, our work uses external knowledge sources to enhance text generation for counseling conversations. external knowledge resources have been found useful for enhancing language models. for example, large-scale commonsense knowledge graphs (cskg) that store structured commonsense knowledge in the form of knowledge triplets. the most widely used cskg resources include concept-net @xcite , atomic @xcite , and transomcs @xcite . there are also medical related knowledge bases such umls @xcite and ohama. 1 we use con-ceptnet for commonsense and decide to collect a counseling knowledge base as general domain medical knowledge bases have a limited amount of knowledge aligning with our needs. ## methodology we present a model that leverages a combination of existing commonsense knowledge resources and domain-specific knowledge derived from the target domain. the workflow is illustrated in figure 3 . ## experiments we choose bart as the backbone network for our generation model. it is a standard seq2seq style transformer which achieved sota on multiple down stream tasks with a bidirectional encoder and a left-to-right decoder, which generalizes both gpt2 and bert. each model is trained with three random seeds. ## conclusion in this paper, we proposed the task of knowledge enhanced counseling reflection generation, and experimented with different ways to introduce knowledge into the reflection generation model using both retrieval and generative settings. we found that both strategies benefit the generation task on various automatic metrics, which is further consolidated by the human evaluation. in addition, we showed that counseling domain knowledge serves as good complementary knowledge source to con-ceptnet. through an ablation study, we found that commonsense related to intentional and causal relationships is essential for the counseling domain.