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Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences.

, , , , , , , и . CoRR, (2022)

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Ankit Mittal

Ankit Goyal

Umeswaran Arunagirinathan

Pattra Arunlakshana

Vinayagam Arunachalam

 

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Shooting the moving target: machine learning in cybersecurity., и . OpML, стр. 13-14. USENIX Association, (2019)Learning Representations for Log Data in Cybersecurity., , , , , и . CSCML, том 10332 из Lecture Notes in Computer Science, стр. 250-268. Springer, (2017)Acquire, adapt, and anticipate: continuous learning to block malicious domains., , , и . IEEE BigData, стр. 1891-1898. IEEE, (2018)Best Practices for Data-Efficient Modeling in NLG: How to Train Production-Ready Neural Models with Less Data., , , , , , , , , и 2 other автор(ы). COLING (Industry), стр. 64-77. International Committee on Computational Linguistics, (2020)Getting to Production with Few-shot Natural Language Generation Models., , , , , , , , , и 2 other автор(ы). SIGDIAL, стр. 66-76. Association for Computational Linguistics, (2021)Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences., , , , , , , и . CoRR, (2022)Building Adaptive Acceptability Classifiers for Neural NLG., , , , , , , , , и 2 other автор(ы). EMNLP (1), стр. 682-697. Association for Computational Linguistics, (2021)Self Healing Capabilities in Polymeric Materials An Introduction to Techniques. International Journal of Trend in Scientific Research and Development, 3 (4): 735-738 (мая 2019)