Multi-Turn Dialogue Generation in E -Commerce Platform with the Context of Historical Dialogue (2020)

Abstract: As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers’ historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller’s historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.

## introduction over the past years, online shopping has experienced incredible growth. in e-commerce platforms, e.g., amazon and taobao, brilliant customer service is becoming increasingly important because of significantly reducing the workload of shop sellers. ideally, sellers should provide highquality responses to address the personal needs of the customers. however, such cost can be prohibitive for most small businesses, which inspires us to be concerned with the multi-turn dialogue current dialogue customer hello. seller i'm grad to service you, dear. seller what can i do for you? customer 1.65 meters tall and weigh 48 kg, which size should i buy? seller in my experience, you may fit the m size. customer aright, when could you send it off? seller as soon as we can. customer will you give me some discount? seller's historical dialogues c1 hello. c2 i'm looking for some help. ## related work previous research on multi-turn dialogue generation @xcite @xcite has drawn a huge amount of attention from academia and industry, which has broader usage scenario than single-turn dialogue generation @xcite . serban et al. (2016); chen et al. (2018); wu et al. (2016) proposed a hierarchical encoderdecoder framework to model all the context utterances which can better grasp the overall information of the dialogues. however, these models are difficult to generalize, and their results are unsatisfied since responses maybe vary a lot for the same question towards different occasions and speakers. recent studies have noticed the problem and try to alleviate it by incorporating helpful external information into response generation, e.g., speakers' emotional information. @xcite . @xcite proposed a review response generation model in the e-commerce platform, which used the reinforcement learning and copy mechanism to fuse external product information, thereby generating informative and diverse responses. @xcite proposed a dialogue generation model considering personality traits such as age, name, and gender. @xcite proposed refnet, which used background descriptions about the target dialogue and used a copy mechanism to copy tokens or semantic units. however, all these models are difficult to generalize in reality because of using different materials, which are not always accessible. different from previous studies, which either simply ignore or selectively consider limited external information, we propose a novel and extensible model which integrates sellers' historical dialogues into a multi-turn dialogue generating process and avoids interference from background noise. to our best knowledge, this is the first attempt to incorporate helpful historical dialogues into multi-turn customer service dialogue generation. ## conditional historical generation given current dialogue d and its r relevant historical dialogues participated by the same seller, i.e., @xmath0, d 2 , ..., d r }, our task aims to generate a high quality response y based on the current dialogue d and its historical dialogues h. in this section, we propose a novel conditional historical generation (chg) model and display its architecture in figure 1 , which consists of four main modules: current dialogue encoder, historical dialogue encoder, response representation encoder and context-response attention decoder. ## experiments in this section, we conduct extensive experiments to study the effectiveness of our approach with both automatic and human evaluation metrics. ## conclusion in this paper, we propose a novel conditional historical generation model for generating highquality multi-turn dialogues in e-commerce scenario. different from previous studies which utilize various external information limited to a specific scenario, our model incorporating historical dialogue information into generation is easy to generalize and applied to practical applications. specifically, we introduce a novel historical dialogue selection strategy to find appropriate historical seller responses for the latest customer question. finally, a gated mechanism is used to fuse the results from both the generation module and copy module. the experimental results on a real-world multi-turn dialogue dataset show the effectiveness of our approach. in the future, we will consider using customer characteristics for generating personalized responses for different customers.