![]() Semantic Parsing: Semantic parsing is the task of mapping natural language text to a logical form (or program) (Zelle and Mooney, 1996 Zettlemoyer and Collins, 2005). Overall, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query position predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subsequent dialog (next response predictor). To address this, we improve the MAPO baseline with simple but important modifications suited to our task. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented policy optimization (MAPO), an existing state of the art RL agent. ![]() 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 subsequent dialog. In response, we define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation. Existing approaches require dialog datasets to explicitly annotate these KB queries-these annotations can be time consuming, and expensive. Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses.
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