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qaAskeR\(^+\): a novel testing method for question answering software via asking recursive questions

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Abstract

Question Answering (QA) is an attractive and challenging area in NLP community. With the development of QA technique, plenty of QA software has been applied in daily human life to provide convenient access of information retrieval. To investigate the performance of QA software, many benchmark datasets have been constructed to provide various test cases. However, current QA software is mainly tested in a reference-based paradigm, in which the expected outputs (labels) of test cases are mandatory to be annotated with much human effort before testing. As a result, neither the just-in-time test during usage nor the extensible test on massive unlabeled real-life data is feasible, which keeps the current testing of QA software from being flexible and sufficient. In this work, we propose a novel testing method, qaAskeR \(^+\), with five new Metamorphic Relations for QA software. qaAskeR \(^+\) does not refer to the annotated labels of test cases. Instead, based on the idea that a correct answer should imply a piece of reliable knowledge that always conforms with any other correct answer, qaAskeR \(^+\) tests QA software by inspecting its behaviors on multiple recursively asked questions that are relevant to the same or some further enriched knowledge. Experimental results show that qaAskeR \(^+\) can reveal quite a few violations that indicate actual answering issues on various mainstream QA software without using any pre-annotated labels.

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Notes

  1. Some studies refer them as “open-domain QA” and “closed-domain QA”.

  2. https://spacy.io/

  3. aux is a non-main verb of the clause, including a modal auxiliary and a form of be, do or have in a periphrastic tense. Details could be found at https://universaldependencies.org/.

  4. The similarity score is defined as \(s^R(a, b)=\min (\)R1Precision(a, b), R1Recall(a, b)), where a and b are two strings while R1Precision and R1Recall are two sub-metrics in ROUGE-1 score (token-wise ROUGE similarity between two sentences).

  5. NatQA includes some questions in miscellaneous and informal forms, e.g., “total number of death row inmates in the us?”.

  6. The official models can be found at their replication packages as follows:

    NSM+h: https://github.com/RichardHGL/WSDM2021_NSM.

    Multi-hop Complex KBQA: https://github.com/lanyunshi/Multi-hopComplexKBQA.

    Macaw: https://github.com/allenai/macaw.

  7. Our method solves the test oracle problem and thus in its practical application, the size of the source test suite for each MR, as well as the eligible ones, can be adjusted freely. In such a case, we report the violation rate to reflect the average violation detection ability of our MRs as their overall effectiveness.

  8. https://www.google.com/

  9. All the questions in MKQA can be answered with the public knowledge from the web.

  10. Based on the search results obtained on April 10, 2021.

  11. Based on the search results obtained on April 8, 2022.

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Acknowledgements

We first sincerely appreciate the positive acknowledgment and the very kind suggestions from the anonymous reviewers for both our conference paper and this extended journal paper. This work was partially supported by the National Natural Science Foundation of China under the grant numbers 62250610224, 61972289, and 61832009. And the numerical calculations in this work have been partially done on the supercomputing system in the Supercomputing Center of Wuhan University.

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    Xiaoyuan Xie, Shuo Jin & Songqiang Chen

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Xie, X., Jin, S. & Chen, S. qaAskeR\(^+\): a novel testing method for question answering software via asking recursive questions. Autom Softw Eng 30, 14 (2023). https://doi.org/10.1007/s10515-023-00380-2

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