CARE : A Clue-guided Assistant for CSR s to Read User Manuals (2024)

Abstract: It is time-saving to build a reading assistant for customer service representations (CSRs) when reading user manuals, especially information-rich ones. Current solutions don’t fit the online custom service scenarios well due to the lack of attention to user questions and possible responses. Hence, we propose to develop a time-saving and careful reading assistant for CSRs, named CARE. It can help the CSRs quickly find proper responses from the user manuals via explicit clue chains. Specifically, each of the clue chains is formed by inferring over the user manuals, starting from the question clue aligned with the user question and ending at a possible response. To overcome the shortage of supervised data, we adopt the self-supervised strategy for model learning. The offline experiment shows that CARE is efficient in automatically inferring accurate responses from the user manual. The online experiment further demonstrates the superiority of CARE to reduce CSRs’ reading burden and keep high service quality, in particular with >35% decrease in time spent and keeping a >0.75 ICC score.

## introduction in e-commerce, human services are always involved -once called, a custom service representation (csr) is required to answer the user's questions based on a related user manual @xcite . this job is not easy, as the csr needs to read the information-rich user manual and compose a proper response to the user, as shown in figure 1 (a ). it will be greatly time-saving if we build a reading assistant for csrs when reading user manuals and seeking possible responses. ## related work reading assistant designing an efficient assistant to reduce the human reading burden is vital in building an enjoyable reading experience for information-rich documents @xcite . a line of work claims that connecting co-reference contents can simplify the reading process @xcite @xcite , for example, highlighting all words related to "insurance" in the user manual of figure 1 . another line of work focuses on marking pre-defined and specific contents in the documents @xcite @xcite , for example, highlighting all "notice" sections in the user manual. although these solutions can direct the reader's focus to the main idea of a document, they can hardly reduce the csrs' burden because these highlighted contents have few connections to the user question and possible responses. to fill this blank, we develop a clue-guided assistant that saves csrs' time by explicitly explaining how to get accurate responses in the user manual. explainable machine comprehension despite growing interest, explainable machine comprehension, which requires the model to expose how to arrive at the final responses/answers given a document, is still in the primary stage @xcite . therefore, most of the existing works are devoted to constructing explanation-supporting benchmarks in machine comprehension @xcite @xcite . however, these annotated explanations only contribute to factoid-style reasoning @xcite @xcite . there is still a lack of explanationsupporting datasets for user manuals, which contain both steps and facts. this motivated us to leverage the self-information in user manuals and design a self-supervised strategy for model learning. specifically, we propose an innovative data construction approach that generates both factual and procedural q&a pairs from user manuals. these augmented data can greatly enhance the model's ability to answer complex user questions. knowledge graph question answering compared with conventional question answering, knowledge graph question answering has the inherent advantage of forming explanations (i.e., inferring path) leading to the answers @xcite . this inspires us to convert raw user manuals into structured graphs. in this way, the comprehension of user manuals becomes explicit inferring over the nodes and edges of the graphs. however, current works only characterize factual knowledge in the knowledge graph @xcite , overlooking the necessity of procedural knowledge for integrated understanding of user manuals. as a remedy, we represent the user manuals as heterogeneous graphs to support the reasoning of responses to complex user questions with the help of both procedural and factual information. heterogeneous graph reasoning reasoning on heterogeneous graphs, which are organized as multiple types of nodes and edges, has been used in various tasks that require inference on complex information @xcite @xcite . for example, @xcite and @xcite proposed cross-view contrastive learning based reasoning algorithms to conduct in-depth reasoning for node classification, node clustering, graph classification, etc. in line with these works, we believe the heterogeneous graph reasoning techniques will also greatly benefit the deep comprehension of a user manual, which contains both factual and procedural types of information. despite the big potential, similar efforts are seldom seen in handling user manuals, which also contain multiple types of information. an exception is @xcite , which is the only surveyed literature representing user manuals as heterogeneous graphs. however, this work uses manually-defined rules to find potential answers to user questions and thus has trouble answering questions beyond the rules. to this end, in this paper, we contribute the primary attempt to learn a heterogeneous graph reasoning model for user manuals and cope with various user questions through explicit inference on both procedural and factual information over graphs. ## methodology the proposed reading assistant care is founded on accurate responses to user questions and explicit explanations of these responses. we model it as inferring over the structured representations (i.e., heterogeneous graphs, as shown in figure 2 1 ) of user manuals given user questions. specifically, we propose a backbone model to infer responses as illustrated in figure 3 . the user question is aligned to the user manual by finding the most related node in the graph, namely the question clue node. starting from the question clue node, we jointly infer procedural and factual clues over the graph until reach the response clue nodes that can compose proper responses to the user question. ## experiments an ideal reading assistant is expected to have two advantages. first, it should be efficient to highlight accurate responses to user questions. thus, we design a response clue reasoning experiment to evaluate the ability of the backbone model of care to infer accurate responses. second, it should save csrs' time at low risk by offering clue chains to guide the csrs to arrive at the responses. we further evaluate care in the real-world scenario by deploying it to train newly-hired csrs, namely, the performance test under online deployment. user manuals are mapped into graphs following @xcite with modifications defined in sec. a. we use the pre-trained pert model foot_1 @xcite to embed user questions and initialize nodes embeddings. the threshold δ is set to 0.5. the backbone model of care is trained on two nvidia p100 gpus with the adam optimizer. the learning rate, beam size, and maximum inference depth are set to 1e -5, 4, and 3, respectively. ## conclusion in this paper, we propose a clue-guided assistant care to help csrs reduce the burdens of reading user manuals by providing accurate responses and explicitly showing explainable paths about how to arrive at these responses. specifically, we design a backbone model that aligns user questions to the nodes in the heterogeneous graphs constructed from the manual and infers response clues through joint reasoning along with both procedural and factual paths. in addition, we alleviate the shortage of dedicated annotated data by conducting selfsupervised learning on the inherent information of user manuals. experimental results show that our proposed backbone model can provide accurate response clues for user questions, and the care assistant can greatly save the time for csrs to answer questions, thereby improving the efficiency and quality of online customer service. we have deployed our assistant to the novice csrs training scenario and are preparing to deploy it to the online customer service applications in the future.