Learning to Map Context-Dependent Sentences to Executable Formal Queries

We propose a context-dependent model to map utterances within an interaction to executable formal queries. To incorporate interaction history, the model maintains an interaction-level encoder that updates after each turn, and can copy sub-sequences of previously predicted queries during generation. Our approach combines implicit and explicit modeling of references between utterances.

The meaning of conversational utterances depends strongly on the history of the interaction. Consider a user querying a flight database using natural language (Figure 1). Given a user utterance, the system must generate a query, execute it, and display results to the user, who then provides the next request. Key to correctly mapping utterances to executable queries is resolving references. For example, the second utterance implicitly depends on the first, and the reference ones in the third utterance explicitly refers to the response to the second utterance. Within an interactive system, this information needs to be composed with mentions of database entries (e.g., Seattle, next Monday) to generate a formal executable representation. In this paper, we propose encoder-decoder models that directly map user utterances to executable queries, while considering the history of the interaction, including both previous utterances and their generated queries.

Reasoning about how the meaning of an utterance depends on the history of the interaction is critical to correctly respond to user requests. As interactions progress, users may omit previously mentioned constraints and entities, and an increasing portion of the utterance meaning must be derived from the interaction history. Figure 2 shows SQL queries for the utterances in Figure 1. As the interaction progresses, the majority of the generated query is derived from the interaction history (underlined), rather than from the current utterance. A key challenge is resolving what past information is incorporated and how. For example, in the figure, the second utterance depends on the set of flights defined by the first, while adding a new constraint. The third utterance further refines this set by adding a constraint to the constraints from both previous utterances. In contrast, the fourth utterance refers only to the first one, and skips the two utterances in between.1 Correctly generating the fourth query requires understanding that the time constraint (at 7pm) can be ignored as it follows an airline constraint that has been replaced.