Abstract
In this paper, we propose to investigate Misleading Inference Generation, a new natural language generation task. The goal is to generate a counterfactual sentence for a context and a factual sentence. This paper proposes a framework based on BART and reinforcement learning for the misleading inference generation task. The experiment results show our model significantly outperforms the compared models, making our solution a necessary and strong baseline for future research toward misleading inference generation.
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References
Bhagavatula, C., et al.: Abductive commonsense reasoning. arXiv preprint arXiv:1908.05739 (2019)
Chung, H.L., Chan, Y.H., Fan, Y.C.: A bert-based distractor generation scheme with multi-tasking and negative answer training strategies. arXiv preprint arXiv:2010.05384 (2020)
Feng, F., Zhang, J., He, X., Zhang, H., Chua, T.S.: Empowering language understanding with counterfactual reasoning. arXiv preprint arXiv:2106.03046 (2021)
Gardner, M., et al.: Evaluating models’ local decision boundaries via contrast sets. arXiv preprint arXiv:2004.02709 (2020)
He, P., Liu, X., Gao, J., Chen, W.: Deberta: decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654 (2020)
Kaushik, D., Hovy, E., Lipton, Z.C.: Learning the difference that makes a difference with counterfactually-augmented data. arXiv preprint arXiv:1909.12434 (2019)
Lewis, M., et al.: Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)
Li, M., et al.: Don’t say that! making inconsistent dialogue unlikely with unlikelihood training. arXiv preprint arXiv:1911.03860 (2019)
Liang, C., Yang, X., Dave, N., Wham, D., Pursel, B., Giles, C.L.: Distractor generation for multiple choice questions using learning to rank. In: Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 284–290 (2018)
Lin, C.Y.: Rouge: A Package for Automatic Evaluation of Summaries. In: Text summarization branches out, pp. 74–81 (2004)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
Qin, L., Bosselut, A., Holtzman, A., Bhagavatula, C., Clark, E., Choi, Y.: Counterfactual story reasoning and generation. arXiv preprint arXiv:1909.04076 (2019)
Ren, S., Deng, Y., He, K., Che, W.: Generating natural language adversarial examples through probability weighted word saliency. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1085–1097 (2019)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Wolf, T., et al.: Huggingface’s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)
Wu, T., Ribeiro, M.T., Heer, J., Weld, D.S.: Polyjuice: automated, general-purpose counterfactual generation (2021)
Zhang, H., Zhou, H., Miao, N., Li, L.: Generating fluent adversarial examples for natural languages. arXiv preprint arXiv:2007.06174 (2020)
Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: Bertscore: evaluating text generation with bert. arXiv preprint arXiv:1904.09675 (2019)
Zhu, Y., et al.: Texygen: a benchmarking platform for text generation models. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1097–1100 (2018)
Zmigrod, R., Mielke, S.J., Wallach, H., Cotterell, R.: Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571 (2019)
Acknowledgement
This work is supported by MOST 110-2634-F-005-006 - project Smart Sustainable New Agriculture Research Center (SMARTer) and MOST Project under grant No. 109-2221-E-005–058-MY3.
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Peng, HY., Chung, HL., Chan, YH., Fan, YC. (2022). Misleading Inference Generation via Proximal Policy Optimization. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_39
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DOI: https://doi.org/10.1007/978-3-031-05933-9_39
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