Unsupervised Learning of KB Queries in Task-Oriented Dialogs

Unsupervised Learning of KB Queries in Task-Oriented Dialogs Dinesh Raghu∗1,2, Nikhil Gupta†3, and Mausam1 1IIT Delhi, New Delhi, India 2IBM Research, New Delhi, India 3LimeChat, Gurgaon, India diraghu1@in.ibm.com, nikhil@limechat.ai, mausam@cse.iitd.ac.in Abstract Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) que- ries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries—these annotations can be time consuming, and ex- pensive. In response, we define the novel prob- lems of predicting the KB query and training the dialog agent, without explicit KB query annotation. For query prediction, we propose a reinforcement learning (RL) baseline, which rewards the generation of those queries whose KB results cover the entities mentioned in sub- sequent dialog. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented pol- icy optimization (MAPO), an existing state of the art RL agent. To address this, we improve the MAPO baseline with simple but impor- tant modifications suited to our task. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query po- sition predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subse- quent dialog (next response predictor). Over- all, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation. ∗D. Raghu is an employee at IBM Research. This work was carried out as part of PhD research at IIT Delhi. †This work was done while Nikhil Gupta was a graduate student at IIT Delhi. 1 Introduction Task-oriented dialog (TOD) systems converse with users to accomplish specific tasks such as restaurant reservation (Henderson et al., 2014b), movie ticket booking (Li et al., 2017), or bus enquiry (Raux et al., 2005). In addition to the ability to converse, it is crucial for TOD systems to learn to formulate knowledge base (KB) queries based on user needs, and generate responses using the query results. An example TOD is shown in Figure 1, where during the conversation (at turn 2), the agent queries the KB based on the user needs, and then suggests the Peking Restaurant based on the retrieved results. Existing end-to-end approaches (Bordes and Weston, 2017; Madotto et al., 2018; Reddy et al., 2019) learn to formulate KB queries using manually annotated queries. In real-world scenarios, human agents chat with users on messaging platforms. When the need to query the KB arises, the agent fires the query on a back-end KB application and uses the retrieved results to compose a response back on the mes- saging platform. The dialogs retrieved from these platforms contain just the user and the agent utterances, but the KB queries typically go un- documented. As existing approaches require KB annotations, they have to be manually annotated, which is expensive and hinders scalability. To eliminate the need …

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