Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 133–142, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 133 Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever Libo Qin, Yijia Liu, Wanxiang Che∗, Haoyang Wen, Yangming Li, Ting Liu Research Center for Social Computing and Information Retrieval Harbin Institute of Technology, China {lbqin,yjliu,car,hywen,yangmingli,tliu}@ir.hit.edu.cn Abstract Querying the knowledge base (KB) has long been a challenge in the end-to-end task- oriented dialogue system. Previous sequence- to-sequence (Seq2Seq) dialogue generation work treats the KB query as an attention over the entire KB, without the guarantee that the generated entities are consistent with each other. In this paper, we propose a novel frame- work which queries the KB in two steps to im- prove the consistency of generated entities. In the first step, inspired by the observation that a response can usually be supported by a single KB row, we introduce a KB retrieval compo- nent which explicitly returns the most relevant KB row given a dialogue history. The retrieval result is further used to filter the irrelevant en- tities in a Seq2Seq response generation model to improve the consistency among the output entities. In the second step, we further per- form the attention mechanism to address the most correlated KB column. Two methods are proposed to make the training feasible without labeled retrieval data, which include distant supervision and Gumbel-Softmax technique. Experiments on two publicly available task oriented dialog datasets show the effective- ness of our model by outperforming the base- line systems and producing entity-consistent responses. 1 Introduction Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation (Sutskever et al., 2014; Bahdanau et al., 2014; Luong et al., 2015a; Nallapati et al., 2016b,a), several works tried to model the task-oriented dialogue as the Seq2Seq generation of response from the dialogue ∗* Email corresponding. Address Distance POI type POI Traffic info 638 Amherst St 3 miles grocery store Sigona Farmers Market car collision nearby 269 Alger Dr 1 miles coffee or tea place Cafe Venetia car collision nearby 5672 barringer street 5 miles certain address 5672 barringer street no traffic 200 Alester Ave 2 miles gas station Valero road block nearby 899 Ames Ct 5 miles hospital Stanford Childrens Health moderate traffic 481 Amaranta Ave 1 miles parking garage Palo Alto Garage R moderate traffic 145 Amherst St 1 miles coffee or tea place Teavana road block nearby 409 Bollard St 5 miles grocery store Willows Market no traffic Driver Address to the gas station. Car Valero is located at 200 Alester Ave. Driver OK , please give me directions via a route that avoids all heavy traffic. Car Since there is a road block nearby, I found another route for you and I sent it on your screen. Driver Awesome thank you. Figure …

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