Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data. While attracting increasing attention, such evaluation metrics are usually developed and evaluated in silo for a single task or dataset, slowing their adoption. Moreover, previous meta-evaluation protocols focused on system-level correlations with human annotations, which leave the example-level accuracy of such metrics unclear. In this work, we introduce TRUE: a comprehensive study of factual consistency metrics on a standardized collection of existing texts from diverse tasks, manually annotated for factual consistency. Our standardization enables an example-level meta-evaluation protocol that is more actionable and interpretable than previously reported correlations, yielding clearer quality measures. Across diverse state-of-the-art metrics and 11 datasets we find that large-scale NLI and question generation-and-answering-based approaches achieve strong and complementary results. We recommend those methods as a starting point for model and metric developers, and hope TRUE will foster progress towards even better methods.
Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias. 2022.TRUE: Re-evaluating Factual Consistency Evaluation. InProceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 161–175, Dublin, Ireland. Association for Computational Linguistics.
@inproceedings{honovich-etal-2022-true, title = "{TRUE}: Re-evaluating Factual Consistency Evaluation", author = "Honovich, Or and Aharoni, Roee and Herzig, Jonathan and Taitelbaum, Hagai and Kukliansy, Doron and Cohen, Vered and Scialom, Thomas and Szpektor, Idan and Hassidim, Avinatan and Matias, Yossi", editor = "Feng, Song and Wan, Hui and Yuan, Caixia and Yu, Han", booktitle = "Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.dialdoc-1.19/", doi = "10.18653/v1/2022.dialdoc-1.19", pages = "161--175", abstract = "Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data. While attracting increasing attention, such evaluation metrics are usually developed and evaluated in silo for a single task or dataset, slowing their adoption. Moreover, previous meta-evaluation protocols focused on system-level correlations with human annotations, which leave the example-level accuracy of such metrics unclear. In this work, we introduce TRUE: a comprehensive study of factual consistency metrics on a standardized collection of existing texts from diverse tasks, manually annotated for factual consistency. Our standardization enables an example-level meta-evaluation protocol that is more actionable and interpretable than previously reported correlations, yielding clearer quality measures. Across diverse state-of-the-art metrics and 11 datasets we find that large-scale NLI and question generation-and-answering-based approaches achieve strong and complementary results. We recommend those methods as a starting point for model and metric developers, and hope TRUE will foster progress towards even better methods."}
%0 Conference Proceedings%T TRUE: Re-evaluating Factual Consistency Evaluation%A Honovich, Or%A Aharoni, Roee%A Herzig, Jonathan%A Taitelbaum, Hagai%A Kukliansy, Doron%A Cohen, Vered%A Scialom, Thomas%A Szpektor, Idan%A Hassidim, Avinatan%A Matias, Yossi%Y Feng, Song%Y Wan, Hui%Y Yuan, Caixia%Y Yu, Han%S Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering%D 2022%8 May%I Association for Computational Linguistics%C Dublin, Ireland%F honovich-etal-2022-true%X Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data. While attracting increasing attention, such evaluation metrics are usually developed and evaluated in silo for a single task or dataset, slowing their adoption. Moreover, previous meta-evaluation protocols focused on system-level correlations with human annotations, which leave the example-level accuracy of such metrics unclear. In this work, we introduce TRUE: a comprehensive study of factual consistency metrics on a standardized collection of existing texts from diverse tasks, manually annotated for factual consistency. Our standardization enables an example-level meta-evaluation protocol that is more actionable and interpretable than previously reported correlations, yielding clearer quality measures. Across diverse state-of-the-art metrics and 11 datasets we find that large-scale NLI and question generation-and-answering-based approaches achieve strong and complementary results. We recommend those methods as a starting point for model and metric developers, and hope TRUE will foster progress towards even better methods.%R 10.18653/v1/2022.dialdoc-1.19%U https://aclanthology.org/2022.dialdoc-1.19/%U https://doi.org/10.18653/v1/2022.dialdoc-1.19%P 161-175
Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias. 2022.TRUE: Re-evaluating Factual Consistency Evaluation. InProceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 161–175, Dublin, Ireland. Association for Computational Linguistics.