Assessing the Robustness of Conversational Agents using Paraphrases

Assessing a conversational agent's understanding capabilities is critical, as poor user interactions could seal the agent's fate at the very beginning of its lifecycle with users abandoning the system. In this paper we explore the use of paraphrases as a testing tool for conversational agents. Paraphrases, which are different ways of expressing the same intent, are generated based on known working input by performing lexical substitutions. As the expected outcome for this newly generated data is known, we can use it to assess the agent's robustness to language variation and detect potential understanding weaknesses. As demonstrated by a case study, we obtain encouraging results as it appears that this approach can help anticipate potential understanding shortcomings and that these shortcomings can be addressed by the generated paraphrases.