Skip to main content
SpringerLink
Log in

Neural Models for Factual Inconsistency Classification with Explanations

  • Conference paper
  • First Online:
Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14171))

Abstract

Factual consistency is one of the most important requirements when editing high quality documents. It is extremely important for automatic text generation systems like summarization, question answering, dialog modeling, and language modeling. Still, automated factual inconsistency detection is rather under-studied. Existing work has focused on (a) finding fake news keeping a knowledge base in context, or (b) detecting broad contradiction (as part of natural language inference literature). However, there has been no work on detecting and explaining types of factual inconsistencies in text, without any knowledge base in context. In this paper, we leverage existing work in linguistics to formally define five types of factual inconsistencies. Based on this categorization, we contribute a novel dataset, FICLE (Factual Inconsistency CLassification with Explanation), with \(\sim \)8K samples where each sample consists of two sentences (claim and context) annotated with type and span of inconsistency. When the inconsistency relates to an entity type, it is labeled as well at two levels (coarse and fine-grained). Further, we leverage this dataset to train a pipeline of four neural models to predict inconsistency type with explanations, given a (claim, context) sentence pair. Explanations include inconsistent claim fact triple, inconsistent context span, inconsistent claim component, coarse and fine-grained inconsistent entity types. The proposed system first predicts inconsistent spans from claim and context; and then uses them to predict inconsistency types and inconsistent entity types (when inconsistency is due to entities). We experiment with multiple Transformer-based natural language classification as well as generative models, and find that DeBERTa performs the best. Our proposed methods provide a weighted F1 of \(\sim \)87% for inconsistency type classification across the five classes. We make the code and dataset publicly available (https://github.com/blitzprecision/FICLE).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/blitzprecision/FICLE.

  2. 2.

    https://labelstud.io/.

  3. 3.

    https://fever.ai/dataset/fever.html.

References

  1. Alhindi, T., Petridis, S., Muresan, S.: Where is your evidence: improving fact-checking by justification modeling. In: Proceedings of the First Workshop on Fact Extraction and Verification (FEVER), pp. 85–90 (2018)

    Google Scholar 

  2. Atanasova, P., Simonsen, J.G., Lioma, C., Augenstein, I.: Generating fact checking explanations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7352–7364 (2020)

    Google Scholar 

  3. Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pp. 632–642. Association for Computational Linguistics (ACL) (2015)

    Google Scholar 

  4. Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large, annotated corpus for learning natural language inference (2015). Preprint at arXiv:1508.05326. Accessed 21 Jun 2021

  5. Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)

    Google Scholar 

  6. Camburu, O.M., Rocktäschel, T., Lukasiewicz, T., Blunsom, P.: e-SNLI: natural language inference with natural language explanations. Adv. Neural. Inf. Process. Syst. 31, 9539–9549 (2018)

    Google Scholar 

  7. Cao, Z., Wei, F., Li, W., Li, S.: Faithful to the original: fact aware neural abstractive summarization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  8. Ciampaglia, G.L., Shiralkar, P., Rocha, L.M., Bollen, J., Menczer, F., Flammini, A.: Computational fact checking from knowledge networks. PLoS ONE 10(6), e0128193 (2015)

    Article  Google Scholar 

  9. De Marneffe, M.C., Rafferty, A.N., Manning, C.D.: Finding contradictions in text. In: Proceedings of ACL-08: HLT, pp. 1039–1047 (2008)

    Google Scholar 

  10. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  11. Dušek, O., Kasner, Z.: Evaluating semantic accuracy of data-to-text generation with natural language inference. In: Proceedings of the 13th International Conference on Natural Language Generation, pp. 131–137 (2020)

    Google Scholar 

  12. He, P., Liu, X., Gao, J., Chen, W.: DEBERTa: decoding-enhanced BERT with disentangled attention. arXiv preprint arXiv:2006.03654 (2020)

  13. Honovich, O., Choshen, L., Aharoni, R., Neeman, E., Szpektor, I., Abend, O.: \(q^2\): evaluating factual consistency in knowledge-grounded dialogues via question generation and question answering. arXiv preprint arXiv:2104.08202 (2021)

  14. Ji, Z., et al.: Survey of hallucination in natural language generation. ACM Computing Surveys p, To appear (2022)

    Google Scholar 

  15. Joshi, P., Aditya, S., Sathe, A., Choudhury, M.: TaxiNLI: taking a ride up the NLU hill. In: Proceedings of the 24th Conference on Computational Natural Language Learning, pp. 41–55 (2020)

    Google Scholar 

  16. Kryściński, W., Keskar, N.S., McCann, B., Xiong, C., Socher, R.: Neural text summarization: a critical evaluation. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 540–551 (2019)

    Google Scholar 

  17. Kumar, S., Talukdar, P.: NILE: natural language inference with faithful natural language explanations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8730–8742 (2020)

    Google Scholar 

  18. Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871–7880 (2020)

    Google Scholar 

  19. Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

  20. Longpre, S., Perisetla, K., Chen, A., Ramesh, N., DuBois, C., Singh, S.: Entity-based knowledge conflicts in question answering. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 7052–7063 (2021)

    Google Scholar 

  21. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

  22. Mao, Y., Ren, X., Ji, H., Han, J.: Constrained abstractive summarization: preserving factual consistency with constrained generation. arXiv preprint arXiv:2010.12723 (2020)

  23. Maynez, J., Narayan, S., Bohnet, B., McDonald, R.: On faithfulness and factuality in abstractive summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1906–1919 (2020)

    Google Scholar 

  24. Mesgar, M., Simpson, E., Gurevych, I.: Improving factual consistency between a response and persona facts. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 549–562 (2021)

    Google Scholar 

  25. Nan, F., et al.: Entity-level factual consistency of abstractive text summarization. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 2727–2733 (2021)

    Google Scholar 

  26. Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., Kiela, D.: Adversarial NLI: a new benchmark for natural language understanding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4885–4901 (2020)

    Google Scholar 

  27. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485–5551 (2020)

    MathSciNet  Google Scholar 

  28. Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

    Google Scholar 

  29. Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  30. Saeed, J.: Semantics. Wiley, Introducing Linguistics (2011)

    Google Scholar 

  31. Shi, B., Weninger, T.: Discriminative predicate path mining for fact checking in knowledge graphs. Knowl.-Based Syst. 104, 123–133 (2016)

    Article  Google Scholar 

  32. Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: Fever: a large-scale dataset for fact extraction and verification. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 809–819 (2018)

    Google Scholar 

  33. Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: Generating token-level explanations for natural language inference. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 963–969 (2019)

    Google Scholar 

  34. Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998–6008 (2017)

    Google Scholar 

  35. Vedula, N., Parthasarathy, S.: FACE-KEG: fact checking explained using knowledge graphs. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 526–534 (2021)

    Google Scholar 

  36. Wang, A., Cho, K., Lewis, M.: Asking and answering questions to evaluate the factual consistency of summaries. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5008–5020 (2020)

    Google Scholar 

  37. Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1112–1122. Association for Computational Linguistics, New Orleans, Louisiana (2018). https://doi.org/10.18653/v1/N18-1101, www.aclanthology.org/N18-1101

  38. Zellers, R., et al.: Defending against neural fake news. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  39. Zhang, S., Niu, J., Wei, C.: Fine-grained factual consistency assessment for abstractive summarization models. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 107–116 (2021)

    Google Scholar 

  40. Zhou, C., Neubig, G., Gu, J., Diab, M., Guzmán, F., Zettlemoyer, L., Ghazvininejad, M.: Detecting hallucinated content in conditional neural sequence generation. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1393–1404 (2021)

    Google Scholar 

  41. Zhu, C., et al.: Enhancing factual consistency of abstractive summarization. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 718–733 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IIIT-Hyderabad, Hyderabad, India

    Tathagata Raha, Mukund Choudhary, Abhinav Menon, Harshit Gupta, K. V. Aditya Srivatsa, Manish Gupta & Vasudeva Varma

  2. Microsoft, Hyderabad, India

    Manish Gupta

Authors
  1. Tathagata Raha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukund Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abhinav Menon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Harshit Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. K. V. Aditya Srivatsa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manish Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vasudeva Varma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manish Gupta .

Editor information

Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

Ethics declarations

Ethical Statement

In this work, we derived a dataset from FEVER datasetFootnote 3. Data annotations in FEVER incorporate material from Wikipedia, which is licensed pursuant to the Wikipedia Copyright Policy. These annotations are made available under the license terms described on the applicable Wikipedia article pages, or, where Wikipedia license terms are unavailable, under the Creative Commons Attribution-ShareAlike License (version 3.0), available at this link: http://creativecommons.org/licenses/by-sa/3.0/. Thus, we made use of the dataset in accordance with its appropriate usage terms. The FICLE dataset does not contain any personally identifiable information. Details of the manual annotations are explained in Sect. 4 as well as in annotationGuidelines.pdf at https://github.com/blitzprecision/FICLE.

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Raha, T. et al. (2023). Neural Models for Factual Inconsistency Classification with Explanations. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43418-1_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43417-4

  • Online ISBN: 978-3-031-43418-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation