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Automatic evaluation of various text quality criteria produced by data-driven intelligent methods is very common and useful because it is cheap, fast, and usually yields repeatable results.
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Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Proc. ACL workshop on Text Summarization Branches Out. p. 10 (2004), http://aclweb.org/anthology/W04-1013
2004
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Banerjee, S., Lavie, A.: METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. pp. 65–72. ACL, Ann Arbor, Michigan (Jun 2005)
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Reiter, E., Belz, A.: An investigation into the validity of some metrics for automatically evaluating natural language generation systems. Computational Linguistics 35
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Bojar, O., Kos, K., Mareček, D.: Tackling sparse data issue in machine translation evaluation. In: Proceedings of the ACL 2010 Conference Short Papers. pp. 86–91. Association for Computational Linguistics, Uppsala, Sweden (Jul 2010)
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Napoles, C., Gormley, M., Van Durme, B.: Annotated gigaword. In: Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction. pp. 95–100. AKBC-WEKEX ’12, Association for Computational Linguistics, Stroudsburg, PA, USA (2012), http://dl.acm.org/citation.cfm?id=2391200.2391218
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Fornaciari, T., Poesio, M.: Identifying fake Amazon reviews as learning from crowds. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. pp. 279–287. Association for Computational Linguistics, Gothenburg, Sweden (Apr 2014). https://doi.org/10.3115/v1/E14-1030
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Hastie, H., Belz, A.: A comparative evaluation methodology for nlg in interactive systems. In: Proceedings of LREC’14. pp. 4004–4011. European Language Resources Association (12 2014)
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Vajjala, S., Meurers, D.: Assessing the relative reading level of sentence pairs for text simplification. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL-14). Association for Computational Linguistics, Gothenburg, Sweden (2014)
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Gkatzia, D., Mahamood, S.: A snapshot of NLG evaluation practices 2005 - 2014. In: Proceedings of the 15th European Workshop on Natural Language Generation (ENLG). pp. 57–60. Association for Computational Linguistics, Brighton, UK (Sep 2015). https://doi.org/10.18653/v1/W15-4708
2015
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Ambati, B.R., Reddy, S., Steedman, M.: Assessing relative sentence complexity using an incremental CCG parser. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics. pp. 1051–1057. ACL, San Diego, California (Jun 2016)
2016
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Nallapati, R., Zhou, B., dos Santos, C., Gulcehre, C., Xiang, B.: Abstractive text summarization using sequence-to-sequence rnns and beyond. In: Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning. pp. 280–290. Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/K16-1028, http://aclweb.org/anthology/K16-1028
2016
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Reiter, E.: A structured review of the validity of BLEU. Computational Linguistics 44
2018
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Sulem, E., Abend, O., Rappoport, A.: BLEU is not suitable for the evaluation of text simplification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 738–744. ACL, Brussels, Belgium (Oct-Nov 2018). https://doi.org/10.18653/v1/D18-1081
2018
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Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing [review article]. IEEE Computational Intelligence Magazine 13
2018
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Çano, E., Bojar, O.: Efficiency metrics for data-driven models: A text summarization case study. In: Proceedings of the 12th International Conference on Natural Language Generation. pp. 229–239. Association for Computational Linguistics, Tokyo, Japan (Oct–Nov 2019). https://doi.org/10.18653/v1/W19-8630, https://www.aclweb.org/anthology/W19-8630
2019
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Novikova, J., Dušek, O., Cercas Curry, A., Rieser, V.: Why we need new evaluation metrics for NLG. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 2241–2252. ACL, Copenhagen, Denmark (Sep 2017). https://doi.org/10.18653/v1/D17-1238
2017
Cited alongside, same era.
Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: A data mining perspective. SIGKDD Explor. Newsl. 19
2017
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Amidei, J., Piwek, P., Willis, A.: Evaluation methodologies in automatic question generation 2013-2018. In: Proceedings of the 11th International Conference on Natural Language Generation. pp. 307–317. Association for Computational Linguistics, Tilburg University, The Netherlands (Nov 2018)
2018
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Balaji, K., Lavanya, K.: Recent Trends in Deep Learning with Applications, pp. 201–222. Springer International Publishing, Cham (2018)
2018
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Pérez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. In: Proceedings of the 27th International Conference on Computational Linguistics. pp. 3391–3401. Association for Computational Linguistics, Santa Fe, New Mexico, USA (Aug 2018), https://www.aclweb.org/anthology/C18-1287
2018
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Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2018), https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf
2018
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Çano, E., Bojar, O.: Keyphrase generation: A multi-aspect survey. In: 2019 25th Conference of Open Innovations Association (FRUCT). pp. 85–94. Helsinki, Finland (Nov 2019). https://doi.org/10.23919/FRUCT48121.2019.8981519, https://ieeexplore.ieee.org/document/8981519
2019
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https://doi.org/10.18653/v1/N19-1423
2019
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Gehrmann, S., Strobelt, H., Rush, A.: GLTR: Statistical detection and visualization of generated text. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. pp. 111–116. Association for Computational Linguistics, Florence, Italy (Jul 2019). https://doi.org/10.18653/v1/P19-3019
2019
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van der Lee, C., Gatt, A., van Miltenburg, E., Wubben, S., Krahmer, E.: Best practices for the human evaluation of automatically generated text. In: Proceedings of the 12th International Conference on Natural Language Generation. pp. 355–368. Association for Computational Linguistics, Tokyo, Japan (Oct–Nov 2019)
2019
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Çano, E., Bojar, O.: Two huge title and keyword generation corpora of research articles. In: Proceedings of The 12th Language Resources and Evaluation Conference. pp. 6663–6671. European Language Resources Association, Marseille, France (may 2020), https://www.aclweb.org/anthology/2020.lrec-1.823
2020
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