Building an Application Independent Natural Language Interface
This addresses the problem of domain-specific retraining and translation in NL interfaces for developers, though it appears incremental as it builds on existing matching techniques.
The paper tackles the challenge of building natural language interfaces by proposing an application-independent approach that avoids semantic parsers and translators, using natural language matching and learning instead, with experimental results demonstrating its effectiveness.
Traditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application. However, it is still challenging for a semantic parser to correctly parse natural language. For a different domain, the parser may need to be retrained or tuned, and a new translator also needs to be written to convert the logical forms to executable actions. In this work, we propose a novel and application independent approach to building NL interfaces that does not need a semantic parser or a translator. It is based on natural language to natural language matching and learning, where the representation of each action and each user command are both in natural language. To perform a user intended action, the system only needs to match the user command with the correct action representation, and then execute the corresponding action. The system also interactively learns new (paraphrased) commands for actions to expand the action representations over time. Our experimental results show the effectiveness of the proposed approach.