Assessing the Robustness of Conversational Agents using Paraphrases
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|Title:||Assessing the Robustness of Conversational Agents using Paraphrases||Authors:||Guichard, Jonathan; Ruane, Elayne; Smith, Ross; Bean, Dan; Ventresque, Anthony||Permanent link:||http://hdl.handle.net/10197/10135||Date:||9-Apr-2019||Online since:||2019-04-24T13:18:29Z||Abstract:||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 per- forming 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.||Funding Details:||Science Foundation Ireland||metadata.dc.description.othersponsorship:||Microsoft Corporation||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2019 IEEE||Keywords:||Testing; Robustness; Tools; Task analysis; Granner; Engines; Software||DOI:||10.1109/AITest.2019.000-7||Other versions:||http://ieeeaitests.com/archive/2019/||Language:||en||Status of Item:||Peer reviewed||Conference Details:||The 2019 IEEE International Conference on Artificial Intelligence Testing (AITest), San Francisco, United States of America, 4-9 April 2019|
|Appears in Collections:||Computer Science Research Collection|
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