Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models

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Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models
Title:
Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models
Journal Title:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Keywords:
Publication Date:
10 December 2023
Citation:
Li, X., Zou, B., Fan, Y., Li, Y., Aw, A. T., & Hong, Y. (2023). Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2023.emnlp-main.209
Abstract:
Conversational Question Answering (CQA) aims to provide natural language answers to users in information-seeking dialogues. Existing CQA benchmarks often evaluate models using pre-collected human-human conversations. However, replacing the model-predicted dialogue history with ground truth compromises the naturalness and sustainability of CQA evaluation. While previous studies proposed using predicted history and rewriting techniques to address unresolved coreferences and incoherencies, this approach renders the question self-contained from the conversation. In this paper, we propose a novel automatic evaluation approach, interview evaluation. Specifically, ChatGPT acts as the interviewer (Q agent) with a set of carefully designed prompts, and the CQA model under test serves as the interviewee (A agent). During the interview evaluation, questions are dynamically generated by the Q agent to guide the A agent in predicting the correct answer through an interactive process. We evaluated four different models on QuAC and two models on CoQA in our experiments. The experiment results demonstrate that our interview evaluation has advantages over previous CQA evaluation approaches, particularly in terms of naturalness and coherence. The source code is made publicly available.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research is supported by core funding from: I2R
Grant Reference no. : CR-2021- 001

The research is supported by National Key R&D Program of China (2020YFB1313601), National Science Foundation of China (62376182, 62076174)
Description:
ISBN:
2023.emnlp-main.209
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